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Age, intelligence, and event-related brain potentials during late childhood
Intelligence 31 (2003) 257 – 274
Age, intelligence, and event-related brain potentials during late childhood: A longitudinal study Johannes E.A. Staudera,*, Maurits W. van der Molenb, Peter C.M. Molenaarb a
Neurocognition Section, Faculty of Psychology, Maastricht University, Postbus 616, 6200 MD, Maastricht, The Netherlands b University of Amsterdam, Amsterdam, The Netherlands
Received 10 August 1998; received in revised form 30 June 2000; accepted 01 November 2000
Abstract The relation between event-related brain activity, age, and intelligence was studied using a visual oddball task presented longitudinally to girls at 9, 10, and 11 years of age. The event-related brain potential (ERP) components showed typical gradual decrements in latency and amplitude with increasing age, but there were also nonlinear changes in event-related brain activity with age. Regression analyses between Raven’s intelligence scores and latency of the ERP components showed negative correlations for the late endogenous components at age 9. At ages 10 and 11, the earlier components showed positive correlations while the later components continued to show negative correlations. The amplitude measures showed only positive correlations and these correlations shifted from the exogenous P1 component at age 9 towards the later endogenous components at ages 10 and 11. The present findings suggest a qualitative shift in the relation between event-related brain activity and intelligence between 9 and 10 years of age. D 2003 Elsevier Science Inc. All rights reserved. Keywords: Event-related potentials; Intelligence; Development
* Corresponding author. Tel.: +31-43-388-1933; fax: +31-43-388-4125. E-mail address: [email protected] (J.E.A. Stauder). 0160-2896/03/$ – see front matter D 2003 Elsevier Science Inc. All rights reserved. PII: S 0 1 6 0 - 2 8 9 6 ( 0 2 ) 0 0 1 3 6 - 8
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1. Introduction Event-related brain potentials (ERPs) are very sensitive indices of the timing and extent of cortical activation during information processing. It is therefore not surprising that many studies reported ERP changes during childhood in a wide variety of tasks. In general, both the latency and the amplitude of the ERP wave components are found to gradually decrease with age (Courchesne, 1978; Friedman, 1991; Stauder, van der Molen, & Molenaar, 1999; Taylor, 1988, 1995; Wijker, 1991). This is often taken to reflect increasing efficiency of the brain processes with age. ERP components occurring between 0 and 150 ms after stimulus onset reflect primarily sensory and early attention processes and are sensitive to physical stimulus features (loudness, brightness, size, etc.). These components are coined exogenous components. Later or endogenous components occur typically between 150 and 2000 ms after stimulus onset and reflect higher cognitive processing, as they are mainly sensitive to changes in task instruction (Donchin, Ritter, & McCallum, 1978). The fact that components become more endogenous with increasing latency is also reflected by their developmental pattern. Exogenous components attain adult levels before age 10, while the endogenous components continue to develop up to senior age level (Goodin, Squires, Henderson, & Starr, 1978). Stauder, Molenaar, and van der Molen (1993) and Stauder, van der Molen, and Molenaar (1995, 1999) found in 5–12-year-old children that specific developmental ERP changes were more associated with sudden shifts in level of cognitive development as measured by a clinical Piagetian test battery than with chronological age. Thus, the developmental ERP literature reports both quantitative ERP changes in latency and amplitude, interpreted as changes in speed and capacity, and qualitative changes in ERP topography, interpreted as changes in strategy and/or implicated brain structures. Clearly, the relation between ERPs and development is complex and far from being fully understood. Our understanding of the relation between ERPs and intelligence is even more troublesome (Deary & Caryl, 1993). However, ERPs offer a real time index of information processing between the presentation of a stimulus and a behavioral response. This renders them very attractive for testing hypotheses concerning the relation between intelligence, information processing, and the brain. In this context, the latency after stimulus onset of an ERP component is taken to reflect the timing of different stages in the information processing chain (for review, see van der Molen, Bashore, Halliday, & Callaway, 1991). The amplitude of ERP components are taken to reflect the investment of effort or capacity demands or differences therein (Kok, 1997). Furthermore, the distribution of the ERP activity across the scalp provides information about the cortical structures involved in the processing of a stimulus or task (Johnson, 1989). The notion that electrical activity recorded from the scalp is modulated by intelligence dates back to the very first publication on the human electroencephalogram (EEG) (Berger, 1929). However, early studies addressing the relationship between EEG and intelligence reported controversial results (see Ellingson 1956, 1966; Vogel & Broverman, 1964, 1966 for a review and discussion). In studying the relationship between intelligence and more specific EEG activity that is time locked to a stimulus or task, some studies did report high correlations with intelligence, but again there were many contradicting findings (for reviews, see Andreassi, 1980; Callaway, 1973; Deary & Caryl, 1993; Gale & Edwards, 1986;
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Stelmack & Houlihan, 1995). On basis of negative correlations between ERP latencies and IQ, Chalke and Ertl (1965) concluded that high IQ persons have fast brains. Shucard and Horn (1972) found that high ability was associated with short ERP latencies to light flashes. Hendrickson (1972) obtained similar findings in both the visual and the auditory domains. However, Callaway (1973) warned in his review paper against such oversimplification because his own and other studies reported the absence or even opposite (positive) correlations between auditory ERP latencies and intelligence. Later on, Hendrikson and Hendrikson (1980) reported impressive correlations (up to .72) between their so-called string measure of ERPs and intelligence in a task were auditory beeps where presented. The string measure treats ERPs like a piece of string and measures its straightened length. Discrepancies in the literature have been explained by referring to (1) recordings from different electrode locations and small numbers of electrodes, (2) small number of participants, (3) large age ranges, (4) lack of replication studies, (5) the use of unstandardized IQ tests, (6) the diversity in experimental paradigms, and (7) the employment of inadequate measures and analyses. More recent studies responded to these criticisms and some notable findings emerged during the last decades. Gasser, Pietz, Schellberg, and Ko¨hler (1988) recorded visual-evoked potentials in children to high-intensity flashes using eight electrodes across the scalp. They reported high IQ to be related with shorter ERP latencies and more negative amplitudes in the 200–250 ms range. This suggested that some time windows of the ERP are more important in the ERP–IQ relation than others. Several studies in children reported that the similarity of the background EEG between different recording sites, so-called EEG coherence, was stronger in low IQ children (Gasser, Jennen-Steinmetz, & Verleger, 1987; Thatcher, McAlaster, Lester, Horst, & Ignasias, 1983). This finding was taken to suggest that brain functioning in children with lower IQ is less differentiated as compared to more intelligent children. A similar interpretation was submitted using event-related desynchronization (ERD), allowing the assessment of short-lasting EEG variations during task performance (Neubauer, Freudenthaler, & Pfurtscheller, 1995). Adults with lower IQ showed diffuse and increased cortical activation in the a-2 band, whereas those with higher IQ revealed a specific activation pattern during performance of the Sentence Verification Test. The more specific activation pattern found for high IQ participants was interpreted as a more efficient use of the brain. The main focus in the present study concerns age-related changes in the relation between intelligence and ERPs. This research was inspired by our findings that ERPs reflect stage transitions in cognitive development (Stauder et al., 1993, 1995). Thus, rendering likely that the relation between ERP components and intelligence is subject to age-related change. Methodological concerns discussed before were taken into account by recording ERPs from 15 locations spread equally across the head to a standard visual oddball task. In general, an oddball task consists of two stimulus conditions that differ from one another on some physical dimension (pitch, intensity, size, etc.) and are presented with different frequencies, thus constituting a ‘‘rare’’ and a ‘‘frequent or standard’’ condition. A visual oddball tasks typically generates a positive peak around 100 ms after stimulus onset at posterior scalp locations, called the P1, which constitutes an index of visuospatial attention (Gunter, Wijers, Jackson, & Mulder, 1994; Harter & Aine, 1984). A second positive peak, the P2, is more prominent at the central-parietal scalp locations and is followed by a negative going N2 component that is most
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prominent at anterior scalp locations. These components are thought to index the mismatch of stimulus contents to a neuronal model (Na¨a¨ta¨nen & Gaillard, 1983). Given that the participant can discriminate successfully between target and standard stimuli and is paying attention, the least frequent (‘‘target’’) condition generates a positive amplitude, between 300 and 1000 ms, called the P3. The amplitude of this P3 component is taken to be proportional to the strength or amount of memory operations required by a task (Curry & Polich, 1992) and its latency is used as a measure of stimulus evaluation time (van der Molen et al., 1991). The level of intelligence was measured by means of the Standard Progressive Matrices (Raven, Court, & Raven, 1983) that is considered to provide an index of the classical general intelligence factor g. The participants averaged 9 years of age at the first recording and participated in two additional successive yearly sessions. This longitudinal design allows for a within-participant replication with age and excludes any cohort differences between age groups. Each year, the children’s SPM score was evaluated and the ERP measurements were taken in the same laboratory and under supervision of the same experimenter. The data were analyzed using conventional analyses of variance of peak latencies and amplitudes, and the relation between ERP measures and intelligence were studied in using linear regression analyses. Many previous ERP intelligence studies performed between-group analyses, for example, in comparing groups with higher versus lower IQ. The problem with this approach is that findings may be biased according the chosen group assignment. This problem is avoided in the present study by resorting to regression analyses.
2. Methods 2.1. Participants Because gender differences can be expected between 9 and 11 years of age, we only included girls to improve the homogeneity of the groups at the cost of reduced generalization of the data. Thirty-six girls (mean age=9.5, S.D.=0.3 years) were recruited by a letter sent to public schools in the greater Amsterdam area. The children were selected with the help of their teachers, and non-Dutch speaking and ill functioning in the classroom were exclusion criteria. Permission for participation was given by caregivers and schools. All children were reported to be in good health and had normal or corrected to normal vision. The longitudinal study comprised three sessions with 1 year between consecutive sessions. Thirty-one girls (mean age=10.6, S.D.=0.2 years) took part in the second session and 26 (mean age=11.5, S.D.=0.3 years) in the third session, resulting in a dropout rate of about 18% a year. At the end of each session, the child received a small gift for her participation. Only children that participated in all three sessions (n=26) are included in the present study. 2.2. Stimuli and instruction Testing took place in a dimly lit and acoustically shielded room. The child reclined in an easy chair at a distance of 1.6 m in front of a back-projection screen. The visual stimuli were
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presented by two Kodak S-AV 2000 projectors having electronic custom-built shutters with rise and fall times of less than 3 ms. Stimuli did not exceed 615 cm and had similar luminance levels. The oddball task presented the child with series of 100 stimuli that were presented for 100 ms with a fixed 2000 ms interval between subsequent stimuli. The series consisted of two different stimuli having unequal probabilities constituting a ‘‘frequent’’ (70%) and ‘‘rare’’ (30%) stimulus condition. The frequent stimulus was a line drawing of a dog and the rare stimulus was a line drawing of a cat, both taken from the standardized Snodgrass and Vanderwart (1980) stimulus set. The child was instructed to silently count the rare stimuli. 2.3. EEG recording The EEG was recorded using an electrocap (Electro-Cap International) from electrode locations placed according the international 10–20 system: F7, Fz, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, and O2. The ground electrode was located 5 cm anterior to Fz and the reference electrode was located at the left mastoid. Electrode impedance was kept below 8 KV. The electrooculogram (EOG) was recorded for removing eye-moment artifacts from the EEG by dynamic regression in the frequency domain (Brillinger, 1975). EOG was recorded with two electrodes, referred to the mastoid, one lateral inferior to the canthus of the right eye and another lateral superior to the canthus of the left eye. Beckman Ag-AgCl electrodes were used for reference and EOG recording. The EEG and EOG were sampled at 100 Hz, starting 210 ms before stimulus and extending 1070 ms after stimulus onset. The time constant of the Nihon-Kohden 4317F polygraph was set at 1 s (0.16 Hz) and the high-frequency cut-off was at 30 Hz. Data acquisition and stimulus presentation were controlled by an IBM-AT interfaced with a Keithley System (Wijker, van der Molen, & Molenaar, 1990). Trials with EEG fluctuations exceeding 200 mV were excluded from further analysis. The child was asked to refrain from moving during task performance and to avoid excessive eye blinks. During testing, the experimenter was in an adjacent room, housing the equipment. The child was monitored by means of an intercom and video circuit. 2.4. Procedure The electrocap, reference, and EOG electrodes were applied while the child was watching a cartoon video. This took about 20 min. Then, the oddball task was explained to the child followed by a practice series of about 25 stimuli to familiarize the child with the task. After completing the ERP task, she was asked to report her mental count of the rare (n=30) stimuli. Then, the Standard Progressive Matrices were completed on an individual basis. Each session was concluded by debriefing the child and she received a small gift for her participation. All sessions were administered in the same experimental rooms, using the same equipment, and under supervision of the same experimenter (first author).
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2.5. ERP scoring Mean, baseline, and EOG-artifact corrected ERPs were calculated for all electrodes and conditions. ERP latency and amplitude measures were determined by a peak-picking procedure for five different peaks: The ‘‘P1’’ peak constituting the maximum amplitude
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between 0 and 150 ms after stimulus onset, the ‘‘P2’’ as the maximum between 170 and 320 ms, the ‘‘N2’’ as the minimum amplitude between 180 and 370 ms, the ‘‘P3’’ as the maximum between 340 and 750 ms, and the ‘‘SW’’ as the minimum amplitude between 600 and 1000 ms. The windows were determined on the basis of visual inspection of poststimulus
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changes in the grand average topography. The P1 component was considered exogenous and the P2, N2, P3, and SW were considered endogenous. For each of the peaks, both the latency and the amplitudes were determined. This resulted in a total of 900 measures (15-electrodes2-conditions3-sessions5-peaks2-peak measures) for each participant. Analyses of variance were performed with sessions (3), task condition (2), and electrodes (15) as between levels and Huyn–Feldt epsilon corrections for degrees of freedom for the averaged F-tests. Linear regression analyses were performed for each peak measure (2), electrode (15), and component (3) separately with the raw SPM score as independent variable. Test–retest reliability of the SPM scores between sessions was examined using Guttman’s split-half procedure. In all analyses, significance levels were set at 5% (two-tailed) and were performed by SPSS for Windows (release 6.1).
3. Results 3.1. SPM and behavioral measures The averages of the raw SPM scores of the 26 girls were 37.15 (S.D.=7.56) at 9.5 years of age (S.D.=0.3), 40.46 (S.D.=6.77) at 10.5 years of age and 43.27 (S.D.=5.49) at 11.5 years of age. This increase in raw SPM scores was significant across sessions and of similar magnitude between 9 and 10 ( F=9.85, P=.004) years and between 10 and 11 years ( F=12.50, P=.002). The SPM test–retest correlations were .73 ( P=.000) between 9 and 10 years and .80 ( P=.000) between 10 and 11 years. The correlation between chronological age (in months) and SPM scores was .48 ( P=.013) at age 9, .47 ( P=.016) at age 10, and just failed to reach significance at the last session (.37, P=.065). All participants reported the correct count of rare stimuli (n=30) in the oddball task at the end of the recording session. 3.2. ERP measures and age: analyses of variance Figures 1 (a and b) depict grand average ERP traces recorded to the rare (Fig. 1a) and frequent (Fig. 1b) stimulus conditions, respectively. The two top traces in each of the figures represent eye movement activity recorded from lateral superior to the canthus of the left eye (EOG1) and lateral inferior to the canthus of the right eye (EOG2). The other traces represent ERP activity recorded at the scalp from frontal areas (F7, Fz, and F8), temporal, central, and parietal electrode locations (T3, C3, Cz, C4, T4, T5, P3, Pz, P4, and T6), and occipital Fig. 1. (a) Grand average ERPs to the rare stimulus condition for the Low- (left column) and High- (right column) scoring group on the SPM. The ERPs represented by the solid lines were longitudinally recorded at 9 years of age, by the dashed lines at 10 years of age, and by the dotted lines at 11 years of age. The vertical lines indicate stimulus onset (0 ms). (b) Grand average ERPs to the frequent stimulus condition for the Low- (left column) and High- (right column) scoring group on the SPM. The ERPs represented by the solid lines were longitudinally recorded at 9 years of age, by the dashed lines at 10 years of age, and by the dotted lines at 11 years of age. The vertical lines indicate stimulus onset (0 ms).
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locations (O1 and O2). The solid lines represent ERPs recorded at 9 years of age, the dashed lines at 10 years of age, and the dotted lines at 11 years of age. The left column depicts the group averages of those children that scored lowest on the SPM across measurement session and right column depicts those children that scored highest on the SPM. In Table 1, it can be observed that all ERP components revealed significant amplitude and latency main effects for Electrodes (EL). This is a common finding and indicates that there Table 1 Repeated-measures analyses of variance for ERP latency (Columns 3 and 4) and amplitude (Columns 5 and 6) of the components P1, P2, N2, P3, and SW with AG, CO, and EL as within factors Components
Variables
F(df ) latency
P-value latency
F(df ) amplitude
P-value amplitude
P1
AG CO EL AGCO AGEL COEL AGCOEL AG CO EL AGCO AGEL COEL AGCOEL AG CO EL AGCO AGEL COEL AGCOEL AG CO EL AGCO AGEL COEL AGCOEL AG CO EL AGCO AGEL COEL AGCOEL
4.04(2,50) 1.76(1,25) 13.36(5.7,142.3) 0.22(1.8,45) 1.53(14.7,367.1) 1.18(8.7,217.4) 1.15(20.8,518.7) 10.28(2,50) 3.96(1,25) 17.92(5.4,135.5) 3.43(2,50) 1.62(15.7,391.4) 2.01(8.6,213.6) 1.87(16.7,414) 1.28(1.7,43.7) 1.26(1,25) 12.86(5.1,127.9) 0.72(2,50) 0.94(20.7,517.4) 4.5(8.1,202.9) 1.44(20.8,521.9) 1.6(1.7,41.5) 0.44(1,25) 22.01(6.8,168.9) 1.39(1.8,43.9) 1.72(17.3,432.8) 4.01(10.1,252.7) 1.82(22.8,570) 0.20(2,50) 10.82(1,25) 12.5(6.5,163.4) 1.85(1.6,39.8) 1.50(20.7,516.7) 2.44(10.1,253.2) 1.35(21.6,541.1)
.024* .197 .000* .780 .095 .313 .289 .000* .058 .000* .040* .064 .043* .020* .286 .272 .000* .492 .537 .000* .093 .216 .514 .000* .259 .036* .000* .012* .821 .003* .000* .176 .072 .008* .132
3.92(1.3,31.6) 1.34(1,25) 30.25(2.7,67.6) 0.16(1.9,48.4) 1.19(6.5,161.81) 1.13(8.1,203) 0.87(11.4,284) 5.55(1.8,46) 10.72(1,25) 37.51(3.9,98.2) 1.06(2,50) 2.50(10.1,251.2) 1.97(7.2,179.4) 1.18(17.4,435.5) 7.74(1.8,44) 0.19(1,25) 18.21(9.8,118.8) 1.27(2,49.8) 2.75(13.6,339.2) 2.17(6.2,156) 1.42(17.1,427.6) 3.14(1.7,41.2) 121.00(1,52) 37.6(7.9,196.6) 0.44(1.8,44.8) 1.5(10.3,256.1) 20.2(7.8,195.1) 1.24(17.2,429.7) 2.51(1.9,46.8) 13.91(1,25) 14.89(7.8,194.1) 0.82(2,50) 3.03(10.6,266.1) 2.50(8.8,220.3) 1.12(12.2,306)
.048* .257 .000* .848 .285 .347 .360 .008* .003* .000* .353 .007* .060 .288 .002* .666 .000* .290 .001* .046* .245 .063 .000* .000* .625 .139 .000* .228 .095 .001* .000* .447 .001* .010* .346
P2
N2
P3
SW
* Significance level p<.050.
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are latency and amplitude differences between electrodes across the scalp. As expected, the stimulus condition effects (CO) were only significant for endogenous components. There was a condition main effect for SW latency, with longer latencies to the rare stimulus, and amplitude main effects for the P2 and P3, with higher amplitudes to the rare stimulus than to the frequent stimulus. COEL interactions revealed a change in ERP scalp distribution between stimulus conditions for N2, P3, and SW latencies and for P3 and SW amplitudes. Latencies were overall longer to the rare stimulus at frontal and parietal electrodes for the N2, at central, parietal, and temporal electrodes for the P3, and at frontal, parietal, and occipital electrodes for the SW. The P3 and SW amplitudes were larger for the rare as compared to the frequent stimuli at the posterior electrodes. For Age (AG), there were main effects for P1 and P2 latencies with a latency dip at age 10 and similar values at the ages 9 and 11. P2 showed also an AGCO interaction and a three-way AgeConditionElectrodes interaction (AGCOEL). These interactions revealed overall latency decrements with age for the rare stimuli and a latency dip at age 10 at the central and parietal electrodes. The latter can be clearly observed in Fig. 1a and b. An AgeElectrodes interaction (AGEL) and an AGCOEL interaction was found for P3 latency showing decreasing latencies with age at the frontal and central electrodes for the rare conditions and increasing latencies with age at Pz for frequent stimuli. Amplitudes showed age main effects for P1, P2, and N2 marked by age-related decrements. An AGEL interaction for the P2 indicated decreasing amplitudes at the anterior, parietal, and temporal electrodes between 9 and 10 years. The N2 showed a similar interaction and an AGCOEL interaction due to a reduction in amplitude at central and parietal electrodes between 10 and 11 years for the rare and at the central electrodes for the frequent stimulus condition. The slow wave (SW) showed an AGEL interaction due to a reduction in amplitude at the posterior electrodes with increasing age. In short, the ERP analyses revealed the typical oddball effects of larger amplitudes and longer latencies for rare as compared to frequent stimuli. These differences were more pronounced for endogenous components (P2, N2, P3, and SW) than for the exogenous P1. With increasing age, there were reductions in latency and amplitude for many of the ERP components, and for both the latency and the amplitude measures, there was a marked dip at age 10. 3.3. ERP measurements and intelligence: regression analyses It is not feasible to depict individual ERPs from of all participants according their SPM score across the three longitudinal recordings. To transmit nevertheless some feel of ERP-IQ changes in time, Fig. 1A and B represents the highest versus the lowest SPM group for three consecutive measuring sessions. Thus, those girls who migrated from the lowest SPM scoring half to the highest, or vice versa, were not included in the averages. This resulted in an average of nine children in each SPM group. 3.4. ERP latency The relation between the ERP measures and intelligence was evaluated by linear regression for each session separately. The results of the regression analyses for the ERP latency
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measures are depicted Fig. 2 and for the ERP amplitude measures in Fig. 3. Only significant correlations are depicted and these correlations ranged between .4 and .6. Fig. 2 shows a topographical representation of the ERP latency measures for the different ages. The electrodes are placed according their approximate location on the scalp (top view, nose on top). The white bars indicate the exogenous component P1 and the black bars indicate the endogenous components P2, N2, P3, and SW. The identity of the endogenous component is depicted by their position on the reference line from the left to the right (see scale top right). Significant correlations with the frequent stimuli are represented upwards and significant correlations to the rare stimuli are represented downwards. The length of the histogram bar is indicative of the strength of the correlation. At age 9 (Fig. 2, top map), all correlations are negative. For the N2, they occur for the rare stimuli (downwards) at Fz and T4 and for the SW for the frequent stimuli (upwards) at P3, P4, O1, and O2. At age 10 (Fig. 2, middle map), the exogenous P1 (white bars) showed positive correlations (indicated by the asterisk) at the occipital and right parietal locations for the frequent stimulus condition. The endogenous P2 also shows negative correlations for the frequent condition at the occipital sites, while the N2 shows negative correlations for both stimulus conditions. For the frequent condition, this component shows a fit at the right frontal electrode F8 and to the rare condition at F8 and the temporal sites T3 and T4. Finally, the P3 shows correlations for the rare condition at central sites and the left posterior temporal site T5. At 11 years of age (Fig. 2, bottom map), the P1 latency again shows positive correlations at the posterior electrodes (*), but now the correlation is significant at T6 for the frequent and at T4 for the rare condition. P2 latency has a positive correlation for the frequent condition at F8. All other later components show negative correlations except for a single positive correlation for P3 amplitude that is significant at F8. In short, at age 9, there are only negative correlations between SPM scores and ERP latency for the components N2 and SW. At ages 10 and 11, there are positive correlations for the earlier components P1 and P2 and mainly negative correlations for the later components N2, P3, and SW. Thus, while increased SPM scores are associated with reduced latencies for the endogenous components at all ages, increased SPM scores relate to longer latencies for the earlier more exogenous components at ages 10 and 11. This suggests the emergence of intelligence-related early brain processes by age 10. 3.5. ERP amplitude The amplitude measures revealed only positive correlations with intelligence (see Fig. 3). At age 9 (Fig. 3, top map), significant correlations occur at the parietal and occipital electrodes for the exogenous P1 for both stimulus types and a single correlation at T6 for the endogenous P3 component to the frequent stimuli. At age 10 (Fig. 3, middle map), there are only significant fits for the endogenous components: right occipital for the P2 to the frequent stimuli, at central and posterior electrodes for the N2 for both stimulus types. The P3 shows correlations at C4 for frequent
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Fig. 2. The regression analyses performed on the latencies of the ERP components P1, P2, N2, P3, and SW. The topographical maps (top view head, nose on top) depict the significant regression fits at ages 9 (top), 10 (middle), and 11 years (bottom). Correlations with the frequent stimulus condition are depicted upwards, and correlations with the rare condition are depicted downwards (see legend on top right). The white histograms indicate the exogenous component P1, and the black histograms indicate the endogenous components P2, N2, P3, and SW. All significant latency regressions are negative, except for those indicated by an asterisk.
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Fig. 3. The regression analyses performed on the amplitudes of the ERP components P1, P2, N2, P3, and SW. The topographical maps (top view head, nose on top) depict the significant regression fits at ages 9 (top), 10 (middle), and 11 years (bottom). Correlations with the frequent stimulus condition are depicted upwards, and correlations with the rare condition are depicted downwards (see legend on top right). The white histograms indicate the exogenous component P1, and the black histograms indicate the endogenous components P2, N2, P3, and SW. All significant amplitude correlations are positive.
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stimuli and T4 for rare stimuli. Finally, the SW shows positive correlations at the central, temporal, and parietal electrodes for the frequent stimuli. At age 11 (Fig. 3, bottom map), there are less correlations as compared to the previous year, but again most correlations occur for the endogenous components N2, P3, and SW and mainly to the frequent stimuli. There are correlations for the N2 at O2 and T6, for the P3 at F8, and for the SW at F8, C4, T5, and Pz. In short, for ERP amplitude, there are only positive correlations with intelligence and these correlations show a remarkable shift from early exogenous components at age 9 toward later endogenous components at ages 10 and 11.
4. Discussion The present results are consistent with previous reports of decrements in ERP latencies and amplitudes with age during childhood and elicited in a standard oddball task (Courchesne, 1978, 1990; Ladish, & Polich, 1989; Mullis, Holcomb, Diner, & Dykman, 1985; Wijker, 1991). The ERPs also showed the typical oddball modulation of increased amplitudes to the rare stimuli for the P3, SW at central and parietal electrode locations and overall longer latencies elicited by the rare stimuli for the N2, P3, and SW. However, we also found nonlinear changes that were not reported before, like a dip at age 10 for P1 and P2 latencies and P2 amplitude. This detection of nonlinear developmental ERP changes may be due to the longitudinal design in the present study that allows for the evaluation of individual developmental profiles. ERP latencies revealed negative correlations with the endogenous components at all ages and a shift towards positive correlations with the earlier exogenous components around age 10. The ERP amplitudes also shifted at the 10 years of age from positive correlations with the exogenous components towards positive correlations with the endogenous components. Thus, the nonlinear dips for P1 and P2 latencies and P2 amplitude at age 10 in the analyses of variance converge with the multiple latency and amplitude shifts in the regression findings. An average of about 10 significant correlations across all components and conditions in relation to the total number of analyses for each age group (n=150) and ERP measure might offer some reason for concern. However, the grouping of the significant correlations around particular brain sites that typically show most important effects for the respective components contradicts a random distribution of significant effects that could be expected on basis of chance. In a combined cross-sectional and longitudinal design, Wijker (1991) reported gradual decrements in ERP amplitude and latency between 5 and 11 years of age but few changes within the 9–11 years age range. Like the present oddball task, children were instructed to silently count the rare stimuli. At difference from the present task, they used not two but four oddball task conditions. Their task included a 55% frequent condition, a 15% rare condition, and two so-called novel conditions (each 15%). Because the present oddball task includes less task conditions, the ERPs to our rare and frequent conditions may have been more sensitive to age-related changes.
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Haier, Robinson, Braden, and Williams (1983) applied the string measure to short ERP epochs. They found positive correlations between Raven’s Advanced Progressive Matrices (RAPM) and string length ranging between .74 and .80 between stimulus onset and 250 ms and concluded that the string measure was an epiphenomenon of a N140-P200 difference. Stough, Nettelbeck, and Cooper (1990) reported significant correlations between the string measure, between 100 and 200 ms after stimulus onset, and Verbal IQ, although no significant relation with RAPM was found. These studies seem to suggest that most relevant relations between ERPs and intelligence are found for early ERP components, between 100 and 250 ms. In the present study, significant correlations with intelligence are indeed often related to components preceding the P3, although P3 and SW components also yield significant correlations. Some studies focused on a similar age range as the present study but addressed only indirectly the issue of intelligence by studying related capacities like memory performance. Howard and Polich (1985) reported negative correlations between P3 latency and memory span in children between 5 and 14 years of age. In their adult participants, they did not find such relation. Thus, like in the present study, they found developmental changes in brain– behavior correlations. Stelmack et al. (1988) reported correlations between ERPs and memory span in 7–12-year-old normal and disabled readers. The correlations were negative for P200 (P2) and P600 amplitude but positive for the N400 amplitude. In a examining the relation between long-term memory and ERPs obtained in auditory and visual stimulation, Bush, Geist, and Emery (1993) reported for adult participants positive correlations with N1, P2, P3, and N4 amplitude to visual stimulation. These correlations ranged from .2 to .3 for the N1, P2, and N4 and reached .45 for P3 amplitude. Taylor and Smith (1995) used 19 electrodes in addressing the relation between ERPs and visual verbal and nonverbal recognition memory tasks in 9–19-year-old children. They reported anterioposterior age-related shifts in P3 topography and decrements in P3 and P4 amplitude and N4, P3, and P4 latency at parietal and temporal electrodes. These effects were most marked for the 9–11 years age range. Overall, these findings suggest that the relation between task performance and ERPs is mostly negative for the latency measures and positive for the amplitude measures and that many changes occur between 9 and 11 years of age. This clearly rounds up with the current findings. The present ERP latency decrements with increasing IQ might be taken to support the speed intelligence hypothesis or ‘‘faster brains have higher IQs’’ as suggested by Chalke and Ertl (1965). Hypotheses that suggest differences in capacity as the basis for individual differences in intelligence (e.g. Case, 1985) are favored by the finding of overall positive correlations between amplitude and IQ. However, unidimensional concepts like speed and capacity in isolation cannot explain a shift in correlation patterns with age. A hypothesis that incorporates both speed and capacity is the neural efficiency hypothesis of intelligence. This hypothesis states that brighter individuals use fewer neurons and neuronal circuits in performing a specific task as compared to less bright individuals. The use of smaller and more efficient circuits increases processing speed and might result in increased ERP amplitudes due to more synchronized and focused activation of the neuronal structures involved. The EEG study by Neubauer et al. (1995), mentioned in Section 1, suggested a similar interpretation of neural efficiency: The ability to focus the cortical resources on those cortical areas that are required for task performance. In addressing Piagetian stage transitions
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and ERPs, Stauder (1992) and Stauder et al. (1993, 1999) suggested that the increments in frontal activity that preceded transitions in cognitive development might reflect a reorganization in neuronal connections towards more efficient structures. On the basis of PET studies, Haier (1993) and Haier et al. (1988) reported a negative relation between brain activity and intelligence. This author stated that a failure of neural pruning during development might result in too many redundant synaptic connections, which consequently might lead to mental retardation. Thus, neural efficiency hypothesis may incorporate developmental changes in the relation between brain activity and intelligence. Unfortunately, the present analyses do not allow for a causal interpretation of a link between the brain, intelligence, and development. It should be noted though that the present study confirmed earlier reports of ERP amplitude to decrease with age, suggesting that smaller brain potentials are a reflection of the brain becoming more efficient. In contrast, the findings of overall positive correlations between intelligence and ERP amplitude suggest that larger amplitudes reflect more efficient brains. These findings at least indicate that these seem to be quite different mechanisms that underlie cognitive development and individual difference in intelligence. In conclusion, the present findings confirm significant correlations between speed and intensity of brain activation and intelligence. In studying the development of these correlations in individual children between 9 and 11 years, there appeared to be an important qualitative shift in this relation around 10 years of age. This suggests that brain structures that are related to intelligence at one point during development may be uncorrelated with intelligence at a later point in time, while other structures become correlated that were not related before. Aside from the theoretical implications of shifts in the relation between brain activity and intelligence with age, this finding may explain some contradicting findings in earlier studies and constitutes an important factor to be taken into account in future research.
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Age, intelligence, and event-related brain potentials during late childhood: A longitudinal study Johannes E.A. Staudera,*, Maurits W. van der Molenb, Peter C.M. Molenaarb a
Neurocognition Section, Faculty of Psychology, Maastricht University, Postbus 616, 6200 MD, Maastricht, The Netherlands b University of Amsterdam, Amsterdam, The Netherlands
Received 10 August 1998; received in revised form 30 June 2000; accepted 01 November 2000
Abstract The relation between event-related brain activity, age, and intelligence was studied using a visual oddball task presented longitudinally to girls at 9, 10, and 11 years of age. The event-related brain potential (ERP) components showed typical gradual decrements in latency and amplitude with increasing age, but there were also nonlinear changes in event-related brain activity with age. Regression analyses between Raven’s intelligence scores and latency of the ERP components showed negative correlations for the late endogenous components at age 9. At ages 10 and 11, the earlier components showed positive correlations while the later components continued to show negative correlations. The amplitude measures showed only positive correlations and these correlations shifted from the exogenous P1 component at age 9 towards the later endogenous components at ages 10 and 11. The present findings suggest a qualitative shift in the relation between event-related brain activity and intelligence between 9 and 10 years of age. D 2003 Elsevier Science Inc. All rights reserved. Keywords: Event-related potentials; Intelligence; Development
* Corresponding author. Tel.: +31-43-388-1933; fax: +31-43-388-4125. E-mail address: [email protected] (J.E.A. Stauder). 0160-2896/03/$ – see front matter D 2003 Elsevier Science Inc. All rights reserved. PII: S 0 1 6 0 - 2 8 9 6 ( 0 2 ) 0 0 1 3 6 - 8
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1. Introduction Event-related brain potentials (ERPs) are very sensitive indices of the timing and extent of cortical activation during information processing. It is therefore not surprising that many studies reported ERP changes during childhood in a wide variety of tasks. In general, both the latency and the amplitude of the ERP wave components are found to gradually decrease with age (Courchesne, 1978; Friedman, 1991; Stauder, van der Molen, & Molenaar, 1999; Taylor, 1988, 1995; Wijker, 1991). This is often taken to reflect increasing efficiency of the brain processes with age. ERP components occurring between 0 and 150 ms after stimulus onset reflect primarily sensory and early attention processes and are sensitive to physical stimulus features (loudness, brightness, size, etc.). These components are coined exogenous components. Later or endogenous components occur typically between 150 and 2000 ms after stimulus onset and reflect higher cognitive processing, as they are mainly sensitive to changes in task instruction (Donchin, Ritter, & McCallum, 1978). The fact that components become more endogenous with increasing latency is also reflected by their developmental pattern. Exogenous components attain adult levels before age 10, while the endogenous components continue to develop up to senior age level (Goodin, Squires, Henderson, & Starr, 1978). Stauder, Molenaar, and van der Molen (1993) and Stauder, van der Molen, and Molenaar (1995, 1999) found in 5–12-year-old children that specific developmental ERP changes were more associated with sudden shifts in level of cognitive development as measured by a clinical Piagetian test battery than with chronological age. Thus, the developmental ERP literature reports both quantitative ERP changes in latency and amplitude, interpreted as changes in speed and capacity, and qualitative changes in ERP topography, interpreted as changes in strategy and/or implicated brain structures. Clearly, the relation between ERPs and development is complex and far from being fully understood. Our understanding of the relation between ERPs and intelligence is even more troublesome (Deary & Caryl, 1993). However, ERPs offer a real time index of information processing between the presentation of a stimulus and a behavioral response. This renders them very attractive for testing hypotheses concerning the relation between intelligence, information processing, and the brain. In this context, the latency after stimulus onset of an ERP component is taken to reflect the timing of different stages in the information processing chain (for review, see van der Molen, Bashore, Halliday, & Callaway, 1991). The amplitude of ERP components are taken to reflect the investment of effort or capacity demands or differences therein (Kok, 1997). Furthermore, the distribution of the ERP activity across the scalp provides information about the cortical structures involved in the processing of a stimulus or task (Johnson, 1989). The notion that electrical activity recorded from the scalp is modulated by intelligence dates back to the very first publication on the human electroencephalogram (EEG) (Berger, 1929). However, early studies addressing the relationship between EEG and intelligence reported controversial results (see Ellingson 1956, 1966; Vogel & Broverman, 1964, 1966 for a review and discussion). In studying the relationship between intelligence and more specific EEG activity that is time locked to a stimulus or task, some studies did report high correlations with intelligence, but again there were many contradicting findings (for reviews, see Andreassi, 1980; Callaway, 1973; Deary & Caryl, 1993; Gale & Edwards, 1986;
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Stelmack & Houlihan, 1995). On basis of negative correlations between ERP latencies and IQ, Chalke and Ertl (1965) concluded that high IQ persons have fast brains. Shucard and Horn (1972) found that high ability was associated with short ERP latencies to light flashes. Hendrickson (1972) obtained similar findings in both the visual and the auditory domains. However, Callaway (1973) warned in his review paper against such oversimplification because his own and other studies reported the absence or even opposite (positive) correlations between auditory ERP latencies and intelligence. Later on, Hendrikson and Hendrikson (1980) reported impressive correlations (up to .72) between their so-called string measure of ERPs and intelligence in a task were auditory beeps where presented. The string measure treats ERPs like a piece of string and measures its straightened length. Discrepancies in the literature have been explained by referring to (1) recordings from different electrode locations and small numbers of electrodes, (2) small number of participants, (3) large age ranges, (4) lack of replication studies, (5) the use of unstandardized IQ tests, (6) the diversity in experimental paradigms, and (7) the employment of inadequate measures and analyses. More recent studies responded to these criticisms and some notable findings emerged during the last decades. Gasser, Pietz, Schellberg, and Ko¨hler (1988) recorded visual-evoked potentials in children to high-intensity flashes using eight electrodes across the scalp. They reported high IQ to be related with shorter ERP latencies and more negative amplitudes in the 200–250 ms range. This suggested that some time windows of the ERP are more important in the ERP–IQ relation than others. Several studies in children reported that the similarity of the background EEG between different recording sites, so-called EEG coherence, was stronger in low IQ children (Gasser, Jennen-Steinmetz, & Verleger, 1987; Thatcher, McAlaster, Lester, Horst, & Ignasias, 1983). This finding was taken to suggest that brain functioning in children with lower IQ is less differentiated as compared to more intelligent children. A similar interpretation was submitted using event-related desynchronization (ERD), allowing the assessment of short-lasting EEG variations during task performance (Neubauer, Freudenthaler, & Pfurtscheller, 1995). Adults with lower IQ showed diffuse and increased cortical activation in the a-2 band, whereas those with higher IQ revealed a specific activation pattern during performance of the Sentence Verification Test. The more specific activation pattern found for high IQ participants was interpreted as a more efficient use of the brain. The main focus in the present study concerns age-related changes in the relation between intelligence and ERPs. This research was inspired by our findings that ERPs reflect stage transitions in cognitive development (Stauder et al., 1993, 1995). Thus, rendering likely that the relation between ERP components and intelligence is subject to age-related change. Methodological concerns discussed before were taken into account by recording ERPs from 15 locations spread equally across the head to a standard visual oddball task. In general, an oddball task consists of two stimulus conditions that differ from one another on some physical dimension (pitch, intensity, size, etc.) and are presented with different frequencies, thus constituting a ‘‘rare’’ and a ‘‘frequent or standard’’ condition. A visual oddball tasks typically generates a positive peak around 100 ms after stimulus onset at posterior scalp locations, called the P1, which constitutes an index of visuospatial attention (Gunter, Wijers, Jackson, & Mulder, 1994; Harter & Aine, 1984). A second positive peak, the P2, is more prominent at the central-parietal scalp locations and is followed by a negative going N2 component that is most
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prominent at anterior scalp locations. These components are thought to index the mismatch of stimulus contents to a neuronal model (Na¨a¨ta¨nen & Gaillard, 1983). Given that the participant can discriminate successfully between target and standard stimuli and is paying attention, the least frequent (‘‘target’’) condition generates a positive amplitude, between 300 and 1000 ms, called the P3. The amplitude of this P3 component is taken to be proportional to the strength or amount of memory operations required by a task (Curry & Polich, 1992) and its latency is used as a measure of stimulus evaluation time (van der Molen et al., 1991). The level of intelligence was measured by means of the Standard Progressive Matrices (Raven, Court, & Raven, 1983) that is considered to provide an index of the classical general intelligence factor g. The participants averaged 9 years of age at the first recording and participated in two additional successive yearly sessions. This longitudinal design allows for a within-participant replication with age and excludes any cohort differences between age groups. Each year, the children’s SPM score was evaluated and the ERP measurements were taken in the same laboratory and under supervision of the same experimenter. The data were analyzed using conventional analyses of variance of peak latencies and amplitudes, and the relation between ERP measures and intelligence were studied in using linear regression analyses. Many previous ERP intelligence studies performed between-group analyses, for example, in comparing groups with higher versus lower IQ. The problem with this approach is that findings may be biased according the chosen group assignment. This problem is avoided in the present study by resorting to regression analyses.
2. Methods 2.1. Participants Because gender differences can be expected between 9 and 11 years of age, we only included girls to improve the homogeneity of the groups at the cost of reduced generalization of the data. Thirty-six girls (mean age=9.5, S.D.=0.3 years) were recruited by a letter sent to public schools in the greater Amsterdam area. The children were selected with the help of their teachers, and non-Dutch speaking and ill functioning in the classroom were exclusion criteria. Permission for participation was given by caregivers and schools. All children were reported to be in good health and had normal or corrected to normal vision. The longitudinal study comprised three sessions with 1 year between consecutive sessions. Thirty-one girls (mean age=10.6, S.D.=0.2 years) took part in the second session and 26 (mean age=11.5, S.D.=0.3 years) in the third session, resulting in a dropout rate of about 18% a year. At the end of each session, the child received a small gift for her participation. Only children that participated in all three sessions (n=26) are included in the present study. 2.2. Stimuli and instruction Testing took place in a dimly lit and acoustically shielded room. The child reclined in an easy chair at a distance of 1.6 m in front of a back-projection screen. The visual stimuli were
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presented by two Kodak S-AV 2000 projectors having electronic custom-built shutters with rise and fall times of less than 3 ms. Stimuli did not exceed 615 cm and had similar luminance levels. The oddball task presented the child with series of 100 stimuli that were presented for 100 ms with a fixed 2000 ms interval between subsequent stimuli. The series consisted of two different stimuli having unequal probabilities constituting a ‘‘frequent’’ (70%) and ‘‘rare’’ (30%) stimulus condition. The frequent stimulus was a line drawing of a dog and the rare stimulus was a line drawing of a cat, both taken from the standardized Snodgrass and Vanderwart (1980) stimulus set. The child was instructed to silently count the rare stimuli. 2.3. EEG recording The EEG was recorded using an electrocap (Electro-Cap International) from electrode locations placed according the international 10–20 system: F7, Fz, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, and O2. The ground electrode was located 5 cm anterior to Fz and the reference electrode was located at the left mastoid. Electrode impedance was kept below 8 KV. The electrooculogram (EOG) was recorded for removing eye-moment artifacts from the EEG by dynamic regression in the frequency domain (Brillinger, 1975). EOG was recorded with two electrodes, referred to the mastoid, one lateral inferior to the canthus of the right eye and another lateral superior to the canthus of the left eye. Beckman Ag-AgCl electrodes were used for reference and EOG recording. The EEG and EOG were sampled at 100 Hz, starting 210 ms before stimulus and extending 1070 ms after stimulus onset. The time constant of the Nihon-Kohden 4317F polygraph was set at 1 s (0.16 Hz) and the high-frequency cut-off was at 30 Hz. Data acquisition and stimulus presentation were controlled by an IBM-AT interfaced with a Keithley System (Wijker, van der Molen, & Molenaar, 1990). Trials with EEG fluctuations exceeding 200 mV were excluded from further analysis. The child was asked to refrain from moving during task performance and to avoid excessive eye blinks. During testing, the experimenter was in an adjacent room, housing the equipment. The child was monitored by means of an intercom and video circuit. 2.4. Procedure The electrocap, reference, and EOG electrodes were applied while the child was watching a cartoon video. This took about 20 min. Then, the oddball task was explained to the child followed by a practice series of about 25 stimuli to familiarize the child with the task. After completing the ERP task, she was asked to report her mental count of the rare (n=30) stimuli. Then, the Standard Progressive Matrices were completed on an individual basis. Each session was concluded by debriefing the child and she received a small gift for her participation. All sessions were administered in the same experimental rooms, using the same equipment, and under supervision of the same experimenter (first author).
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2.5. ERP scoring Mean, baseline, and EOG-artifact corrected ERPs were calculated for all electrodes and conditions. ERP latency and amplitude measures were determined by a peak-picking procedure for five different peaks: The ‘‘P1’’ peak constituting the maximum amplitude
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between 0 and 150 ms after stimulus onset, the ‘‘P2’’ as the maximum between 170 and 320 ms, the ‘‘N2’’ as the minimum amplitude between 180 and 370 ms, the ‘‘P3’’ as the maximum between 340 and 750 ms, and the ‘‘SW’’ as the minimum amplitude between 600 and 1000 ms. The windows were determined on the basis of visual inspection of poststimulus
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changes in the grand average topography. The P1 component was considered exogenous and the P2, N2, P3, and SW were considered endogenous. For each of the peaks, both the latency and the amplitudes were determined. This resulted in a total of 900 measures (15-electrodes2-conditions3-sessions5-peaks2-peak measures) for each participant. Analyses of variance were performed with sessions (3), task condition (2), and electrodes (15) as between levels and Huyn–Feldt epsilon corrections for degrees of freedom for the averaged F-tests. Linear regression analyses were performed for each peak measure (2), electrode (15), and component (3) separately with the raw SPM score as independent variable. Test–retest reliability of the SPM scores between sessions was examined using Guttman’s split-half procedure. In all analyses, significance levels were set at 5% (two-tailed) and were performed by SPSS for Windows (release 6.1).
3. Results 3.1. SPM and behavioral measures The averages of the raw SPM scores of the 26 girls were 37.15 (S.D.=7.56) at 9.5 years of age (S.D.=0.3), 40.46 (S.D.=6.77) at 10.5 years of age and 43.27 (S.D.=5.49) at 11.5 years of age. This increase in raw SPM scores was significant across sessions and of similar magnitude between 9 and 10 ( F=9.85, P=.004) years and between 10 and 11 years ( F=12.50, P=.002). The SPM test–retest correlations were .73 ( P=.000) between 9 and 10 years and .80 ( P=.000) between 10 and 11 years. The correlation between chronological age (in months) and SPM scores was .48 ( P=.013) at age 9, .47 ( P=.016) at age 10, and just failed to reach significance at the last session (.37, P=.065). All participants reported the correct count of rare stimuli (n=30) in the oddball task at the end of the recording session. 3.2. ERP measures and age: analyses of variance Figures 1 (a and b) depict grand average ERP traces recorded to the rare (Fig. 1a) and frequent (Fig. 1b) stimulus conditions, respectively. The two top traces in each of the figures represent eye movement activity recorded from lateral superior to the canthus of the left eye (EOG1) and lateral inferior to the canthus of the right eye (EOG2). The other traces represent ERP activity recorded at the scalp from frontal areas (F7, Fz, and F8), temporal, central, and parietal electrode locations (T3, C3, Cz, C4, T4, T5, P3, Pz, P4, and T6), and occipital Fig. 1. (a) Grand average ERPs to the rare stimulus condition for the Low- (left column) and High- (right column) scoring group on the SPM. The ERPs represented by the solid lines were longitudinally recorded at 9 years of age, by the dashed lines at 10 years of age, and by the dotted lines at 11 years of age. The vertical lines indicate stimulus onset (0 ms). (b) Grand average ERPs to the frequent stimulus condition for the Low- (left column) and High- (right column) scoring group on the SPM. The ERPs represented by the solid lines were longitudinally recorded at 9 years of age, by the dashed lines at 10 years of age, and by the dotted lines at 11 years of age. The vertical lines indicate stimulus onset (0 ms).
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locations (O1 and O2). The solid lines represent ERPs recorded at 9 years of age, the dashed lines at 10 years of age, and the dotted lines at 11 years of age. The left column depicts the group averages of those children that scored lowest on the SPM across measurement session and right column depicts those children that scored highest on the SPM. In Table 1, it can be observed that all ERP components revealed significant amplitude and latency main effects for Electrodes (EL). This is a common finding and indicates that there Table 1 Repeated-measures analyses of variance for ERP latency (Columns 3 and 4) and amplitude (Columns 5 and 6) of the components P1, P2, N2, P3, and SW with AG, CO, and EL as within factors Components
Variables
F(df ) latency
P-value latency
F(df ) amplitude
P-value amplitude
P1
AG CO EL AGCO AGEL COEL AGCOEL AG CO EL AGCO AGEL COEL AGCOEL AG CO EL AGCO AGEL COEL AGCOEL AG CO EL AGCO AGEL COEL AGCOEL AG CO EL AGCO AGEL COEL AGCOEL
4.04(2,50) 1.76(1,25) 13.36(5.7,142.3) 0.22(1.8,45) 1.53(14.7,367.1) 1.18(8.7,217.4) 1.15(20.8,518.7) 10.28(2,50) 3.96(1,25) 17.92(5.4,135.5) 3.43(2,50) 1.62(15.7,391.4) 2.01(8.6,213.6) 1.87(16.7,414) 1.28(1.7,43.7) 1.26(1,25) 12.86(5.1,127.9) 0.72(2,50) 0.94(20.7,517.4) 4.5(8.1,202.9) 1.44(20.8,521.9) 1.6(1.7,41.5) 0.44(1,25) 22.01(6.8,168.9) 1.39(1.8,43.9) 1.72(17.3,432.8) 4.01(10.1,252.7) 1.82(22.8,570) 0.20(2,50) 10.82(1,25) 12.5(6.5,163.4) 1.85(1.6,39.8) 1.50(20.7,516.7) 2.44(10.1,253.2) 1.35(21.6,541.1)
.024* .197 .000* .780 .095 .313 .289 .000* .058 .000* .040* .064 .043* .020* .286 .272 .000* .492 .537 .000* .093 .216 .514 .000* .259 .036* .000* .012* .821 .003* .000* .176 .072 .008* .132
3.92(1.3,31.6) 1.34(1,25) 30.25(2.7,67.6) 0.16(1.9,48.4) 1.19(6.5,161.81) 1.13(8.1,203) 0.87(11.4,284) 5.55(1.8,46) 10.72(1,25) 37.51(3.9,98.2) 1.06(2,50) 2.50(10.1,251.2) 1.97(7.2,179.4) 1.18(17.4,435.5) 7.74(1.8,44) 0.19(1,25) 18.21(9.8,118.8) 1.27(2,49.8) 2.75(13.6,339.2) 2.17(6.2,156) 1.42(17.1,427.6) 3.14(1.7,41.2) 121.00(1,52) 37.6(7.9,196.6) 0.44(1.8,44.8) 1.5(10.3,256.1) 20.2(7.8,195.1) 1.24(17.2,429.7) 2.51(1.9,46.8) 13.91(1,25) 14.89(7.8,194.1) 0.82(2,50) 3.03(10.6,266.1) 2.50(8.8,220.3) 1.12(12.2,306)
.048* .257 .000* .848 .285 .347 .360 .008* .003* .000* .353 .007* .060 .288 .002* .666 .000* .290 .001* .046* .245 .063 .000* .000* .625 .139 .000* .228 .095 .001* .000* .447 .001* .010* .346
P2
N2
P3
SW
* Significance level p<.050.
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are latency and amplitude differences between electrodes across the scalp. As expected, the stimulus condition effects (CO) were only significant for endogenous components. There was a condition main effect for SW latency, with longer latencies to the rare stimulus, and amplitude main effects for the P2 and P3, with higher amplitudes to the rare stimulus than to the frequent stimulus. COEL interactions revealed a change in ERP scalp distribution between stimulus conditions for N2, P3, and SW latencies and for P3 and SW amplitudes. Latencies were overall longer to the rare stimulus at frontal and parietal electrodes for the N2, at central, parietal, and temporal electrodes for the P3, and at frontal, parietal, and occipital electrodes for the SW. The P3 and SW amplitudes were larger for the rare as compared to the frequent stimuli at the posterior electrodes. For Age (AG), there were main effects for P1 and P2 latencies with a latency dip at age 10 and similar values at the ages 9 and 11. P2 showed also an AGCO interaction and a three-way AgeConditionElectrodes interaction (AGCOEL). These interactions revealed overall latency decrements with age for the rare stimuli and a latency dip at age 10 at the central and parietal electrodes. The latter can be clearly observed in Fig. 1a and b. An AgeElectrodes interaction (AGEL) and an AGCOEL interaction was found for P3 latency showing decreasing latencies with age at the frontal and central electrodes for the rare conditions and increasing latencies with age at Pz for frequent stimuli. Amplitudes showed age main effects for P1, P2, and N2 marked by age-related decrements. An AGEL interaction for the P2 indicated decreasing amplitudes at the anterior, parietal, and temporal electrodes between 9 and 10 years. The N2 showed a similar interaction and an AGCOEL interaction due to a reduction in amplitude at central and parietal electrodes between 10 and 11 years for the rare and at the central electrodes for the frequent stimulus condition. The slow wave (SW) showed an AGEL interaction due to a reduction in amplitude at the posterior electrodes with increasing age. In short, the ERP analyses revealed the typical oddball effects of larger amplitudes and longer latencies for rare as compared to frequent stimuli. These differences were more pronounced for endogenous components (P2, N2, P3, and SW) than for the exogenous P1. With increasing age, there were reductions in latency and amplitude for many of the ERP components, and for both the latency and the amplitude measures, there was a marked dip at age 10. 3.3. ERP measurements and intelligence: regression analyses It is not feasible to depict individual ERPs from of all participants according their SPM score across the three longitudinal recordings. To transmit nevertheless some feel of ERP-IQ changes in time, Fig. 1A and B represents the highest versus the lowest SPM group for three consecutive measuring sessions. Thus, those girls who migrated from the lowest SPM scoring half to the highest, or vice versa, were not included in the averages. This resulted in an average of nine children in each SPM group. 3.4. ERP latency The relation between the ERP measures and intelligence was evaluated by linear regression for each session separately. The results of the regression analyses for the ERP latency
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measures are depicted Fig. 2 and for the ERP amplitude measures in Fig. 3. Only significant correlations are depicted and these correlations ranged between .4 and .6. Fig. 2 shows a topographical representation of the ERP latency measures for the different ages. The electrodes are placed according their approximate location on the scalp (top view, nose on top). The white bars indicate the exogenous component P1 and the black bars indicate the endogenous components P2, N2, P3, and SW. The identity of the endogenous component is depicted by their position on the reference line from the left to the right (see scale top right). Significant correlations with the frequent stimuli are represented upwards and significant correlations to the rare stimuli are represented downwards. The length of the histogram bar is indicative of the strength of the correlation. At age 9 (Fig. 2, top map), all correlations are negative. For the N2, they occur for the rare stimuli (downwards) at Fz and T4 and for the SW for the frequent stimuli (upwards) at P3, P4, O1, and O2. At age 10 (Fig. 2, middle map), the exogenous P1 (white bars) showed positive correlations (indicated by the asterisk) at the occipital and right parietal locations for the frequent stimulus condition. The endogenous P2 also shows negative correlations for the frequent condition at the occipital sites, while the N2 shows negative correlations for both stimulus conditions. For the frequent condition, this component shows a fit at the right frontal electrode F8 and to the rare condition at F8 and the temporal sites T3 and T4. Finally, the P3 shows correlations for the rare condition at central sites and the left posterior temporal site T5. At 11 years of age (Fig. 2, bottom map), the P1 latency again shows positive correlations at the posterior electrodes (*), but now the correlation is significant at T6 for the frequent and at T4 for the rare condition. P2 latency has a positive correlation for the frequent condition at F8. All other later components show negative correlations except for a single positive correlation for P3 amplitude that is significant at F8. In short, at age 9, there are only negative correlations between SPM scores and ERP latency for the components N2 and SW. At ages 10 and 11, there are positive correlations for the earlier components P1 and P2 and mainly negative correlations for the later components N2, P3, and SW. Thus, while increased SPM scores are associated with reduced latencies for the endogenous components at all ages, increased SPM scores relate to longer latencies for the earlier more exogenous components at ages 10 and 11. This suggests the emergence of intelligence-related early brain processes by age 10. 3.5. ERP amplitude The amplitude measures revealed only positive correlations with intelligence (see Fig. 3). At age 9 (Fig. 3, top map), significant correlations occur at the parietal and occipital electrodes for the exogenous P1 for both stimulus types and a single correlation at T6 for the endogenous P3 component to the frequent stimuli. At age 10 (Fig. 3, middle map), there are only significant fits for the endogenous components: right occipital for the P2 to the frequent stimuli, at central and posterior electrodes for the N2 for both stimulus types. The P3 shows correlations at C4 for frequent
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Fig. 2. The regression analyses performed on the latencies of the ERP components P1, P2, N2, P3, and SW. The topographical maps (top view head, nose on top) depict the significant regression fits at ages 9 (top), 10 (middle), and 11 years (bottom). Correlations with the frequent stimulus condition are depicted upwards, and correlations with the rare condition are depicted downwards (see legend on top right). The white histograms indicate the exogenous component P1, and the black histograms indicate the endogenous components P2, N2, P3, and SW. All significant latency regressions are negative, except for those indicated by an asterisk.
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Fig. 3. The regression analyses performed on the amplitudes of the ERP components P1, P2, N2, P3, and SW. The topographical maps (top view head, nose on top) depict the significant regression fits at ages 9 (top), 10 (middle), and 11 years (bottom). Correlations with the frequent stimulus condition are depicted upwards, and correlations with the rare condition are depicted downwards (see legend on top right). The white histograms indicate the exogenous component P1, and the black histograms indicate the endogenous components P2, N2, P3, and SW. All significant amplitude correlations are positive.
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stimuli and T4 for rare stimuli. Finally, the SW shows positive correlations at the central, temporal, and parietal electrodes for the frequent stimuli. At age 11 (Fig. 3, bottom map), there are less correlations as compared to the previous year, but again most correlations occur for the endogenous components N2, P3, and SW and mainly to the frequent stimuli. There are correlations for the N2 at O2 and T6, for the P3 at F8, and for the SW at F8, C4, T5, and Pz. In short, for ERP amplitude, there are only positive correlations with intelligence and these correlations show a remarkable shift from early exogenous components at age 9 toward later endogenous components at ages 10 and 11.
4. Discussion The present results are consistent with previous reports of decrements in ERP latencies and amplitudes with age during childhood and elicited in a standard oddball task (Courchesne, 1978, 1990; Ladish, & Polich, 1989; Mullis, Holcomb, Diner, & Dykman, 1985; Wijker, 1991). The ERPs also showed the typical oddball modulation of increased amplitudes to the rare stimuli for the P3, SW at central and parietal electrode locations and overall longer latencies elicited by the rare stimuli for the N2, P3, and SW. However, we also found nonlinear changes that were not reported before, like a dip at age 10 for P1 and P2 latencies and P2 amplitude. This detection of nonlinear developmental ERP changes may be due to the longitudinal design in the present study that allows for the evaluation of individual developmental profiles. ERP latencies revealed negative correlations with the endogenous components at all ages and a shift towards positive correlations with the earlier exogenous components around age 10. The ERP amplitudes also shifted at the 10 years of age from positive correlations with the exogenous components towards positive correlations with the endogenous components. Thus, the nonlinear dips for P1 and P2 latencies and P2 amplitude at age 10 in the analyses of variance converge with the multiple latency and amplitude shifts in the regression findings. An average of about 10 significant correlations across all components and conditions in relation to the total number of analyses for each age group (n=150) and ERP measure might offer some reason for concern. However, the grouping of the significant correlations around particular brain sites that typically show most important effects for the respective components contradicts a random distribution of significant effects that could be expected on basis of chance. In a combined cross-sectional and longitudinal design, Wijker (1991) reported gradual decrements in ERP amplitude and latency between 5 and 11 years of age but few changes within the 9–11 years age range. Like the present oddball task, children were instructed to silently count the rare stimuli. At difference from the present task, they used not two but four oddball task conditions. Their task included a 55% frequent condition, a 15% rare condition, and two so-called novel conditions (each 15%). Because the present oddball task includes less task conditions, the ERPs to our rare and frequent conditions may have been more sensitive to age-related changes.
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Haier, Robinson, Braden, and Williams (1983) applied the string measure to short ERP epochs. They found positive correlations between Raven’s Advanced Progressive Matrices (RAPM) and string length ranging between .74 and .80 between stimulus onset and 250 ms and concluded that the string measure was an epiphenomenon of a N140-P200 difference. Stough, Nettelbeck, and Cooper (1990) reported significant correlations between the string measure, between 100 and 200 ms after stimulus onset, and Verbal IQ, although no significant relation with RAPM was found. These studies seem to suggest that most relevant relations between ERPs and intelligence are found for early ERP components, between 100 and 250 ms. In the present study, significant correlations with intelligence are indeed often related to components preceding the P3, although P3 and SW components also yield significant correlations. Some studies focused on a similar age range as the present study but addressed only indirectly the issue of intelligence by studying related capacities like memory performance. Howard and Polich (1985) reported negative correlations between P3 latency and memory span in children between 5 and 14 years of age. In their adult participants, they did not find such relation. Thus, like in the present study, they found developmental changes in brain– behavior correlations. Stelmack et al. (1988) reported correlations between ERPs and memory span in 7–12-year-old normal and disabled readers. The correlations were negative for P200 (P2) and P600 amplitude but positive for the N400 amplitude. In a examining the relation between long-term memory and ERPs obtained in auditory and visual stimulation, Bush, Geist, and Emery (1993) reported for adult participants positive correlations with N1, P2, P3, and N4 amplitude to visual stimulation. These correlations ranged from .2 to .3 for the N1, P2, and N4 and reached .45 for P3 amplitude. Taylor and Smith (1995) used 19 electrodes in addressing the relation between ERPs and visual verbal and nonverbal recognition memory tasks in 9–19-year-old children. They reported anterioposterior age-related shifts in P3 topography and decrements in P3 and P4 amplitude and N4, P3, and P4 latency at parietal and temporal electrodes. These effects were most marked for the 9–11 years age range. Overall, these findings suggest that the relation between task performance and ERPs is mostly negative for the latency measures and positive for the amplitude measures and that many changes occur between 9 and 11 years of age. This clearly rounds up with the current findings. The present ERP latency decrements with increasing IQ might be taken to support the speed intelligence hypothesis or ‘‘faster brains have higher IQs’’ as suggested by Chalke and Ertl (1965). Hypotheses that suggest differences in capacity as the basis for individual differences in intelligence (e.g. Case, 1985) are favored by the finding of overall positive correlations between amplitude and IQ. However, unidimensional concepts like speed and capacity in isolation cannot explain a shift in correlation patterns with age. A hypothesis that incorporates both speed and capacity is the neural efficiency hypothesis of intelligence. This hypothesis states that brighter individuals use fewer neurons and neuronal circuits in performing a specific task as compared to less bright individuals. The use of smaller and more efficient circuits increases processing speed and might result in increased ERP amplitudes due to more synchronized and focused activation of the neuronal structures involved. The EEG study by Neubauer et al. (1995), mentioned in Section 1, suggested a similar interpretation of neural efficiency: The ability to focus the cortical resources on those cortical areas that are required for task performance. In addressing Piagetian stage transitions
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and ERPs, Stauder (1992) and Stauder et al. (1993, 1999) suggested that the increments in frontal activity that preceded transitions in cognitive development might reflect a reorganization in neuronal connections towards more efficient structures. On the basis of PET studies, Haier (1993) and Haier et al. (1988) reported a negative relation between brain activity and intelligence. This author stated that a failure of neural pruning during development might result in too many redundant synaptic connections, which consequently might lead to mental retardation. Thus, neural efficiency hypothesis may incorporate developmental changes in the relation between brain activity and intelligence. Unfortunately, the present analyses do not allow for a causal interpretation of a link between the brain, intelligence, and development. It should be noted though that the present study confirmed earlier reports of ERP amplitude to decrease with age, suggesting that smaller brain potentials are a reflection of the brain becoming more efficient. In contrast, the findings of overall positive correlations between intelligence and ERP amplitude suggest that larger amplitudes reflect more efficient brains. These findings at least indicate that these seem to be quite different mechanisms that underlie cognitive development and individual difference in intelligence. In conclusion, the present findings confirm significant correlations between speed and intensity of brain activation and intelligence. In studying the development of these correlations in individual children between 9 and 11 years, there appeared to be an important qualitative shift in this relation around 10 years of age. This suggests that brain structures that are related to intelligence at one point during development may be uncorrelated with intelligence at a later point in time, while other structures become correlated that were not related before. Aside from the theoretical implications of shifts in the relation between brain activity and intelligence with age, this finding may explain some contradicting findings in earlier studies and constitutes an important factor to be taken into account in future research.
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