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NeuroImage 29 (2006) 822 – 837

Evidence for developmental changes in the visual word processing

network beyond adolescence

Silvia Brem,a Kerstin Bucher,b Pascal Halder,a Paul Summers,c Thomas Dietrich,b

Ernst Martin,b and Daniel Brandeisa,*

aDepartment of Child and Adolescent Psychiatry, University of Zurich, Brain Mapping Research, Neumunsterallee 9/ Fach, CH-8032 Zurich, SwitzerlandbMR-Center, University Children’s Hospital, Zurich, SwitzerlandcInstitute of Neuroradiology, University Hospital Zurich, Zurich, Switzerland

Received 15 February 2005; revised 10 June 2005; accepted 2 September 2005

Available online 28 October 2005

Late development of specialization in the visual word processing system

was examined using event-related potentials (ERP) and functional

magnetic resonance imaging (fMRI) of word and symbol string

processing in groups of adolescents (15.2–17.3 years) and adults

(19.8–30.8 years). We focused our ERP analyses on fast visual activity:

the occipital P1 (82 –131 ms) modulated by physical stimulus

characteristics and the occipito-temporal N1 (132–256 ms) reflecting

visual tuning for print. Our fMRI analyses concentrated on basal

occipito-temporal activations in the visual word form area VWFA. For

words, the correlation of fMRI activation in the VWFA and N1

amplitude confirmed the close relationship of the electrophysiological

N1 with metabolic activity in the VWFA. Further support for this

relationship came from low resolution electromagnetic tomography

localizing the word-specific N1 near the VWFA.

Both imaging techniques revealed age-independent differences

between words and symbol strings. Late development, however, was

preferentially detected with ERPs. Decreases of P1 and N1 amplitudes

with age were not limited to words and suggested further maturation of

the underlying brain microstructure and function. Following adoles-

cence, decreasing N1 latencies specific to words point to continued

specialization of the visual word processing system. Both N1 and fMRI

measures correlated with reading performance. In summary, the

similarity of global fMRI activation patterns between groups suggests

a fully established distribution of the reading network in adolescence,

while the decreasing N1 latencies for words indicate protracted fine

tuning after adolescence.

D 2005 Elsevier Inc. All rights reserved.

Introduction

Reading is one of the most important forms of human

communication in modern culture and an essential activity in

everyday life. The investigation of the normal progression of

1053-8119/$ - see front matter D 2005 Elsevier Inc. All rights reserved.

doi:10.1016/j.neuroimage.2005.09.023

* Corresponding author. Fax: +41 43 499 2604.

E-mail address: brandeis@kjpd.unizh.ch (D. Brandeis).

Available online on ScienceDirect (www.sciencedirect.com).

reading development is key to being able to detect potential

developmental reading disorders at an early stage so that support

with established training programs, adjusted for the skills and level

of the reader, can be provided as early as possible. Although

reading is of enormous importance in our society, it is not an innate

ability and has to be learned through extensive training at school.

Children usually begin with reading exercises in their first year at

school (5–7 years), and it is during this time that specific brain

regions begin to adopt the functions necessary for processing

written language and a specialized reading network is established

(Maurer et al., 2005a). This network is devoted to fast and partly

automatic word processing steps such as orthographic coding and

the association with sound (phonology), which eventually permit

lexical access and understanding of meaning (semantics). Destruc-

tion of parts in the network through a lesion or impairments in its

functionality result in severe reading disorders (Dejerine, 1892;

Damasio and Damasio, 1983; Rumsey et al., 1992; Shaywitz,

1996; Demb et al., 1998; Shaywitz et al., 2002).

Neurophysiological magnetoencephalographic (MEG) and

electroencephalographic (EEG) studies (Nobre et al., 1994; Helen-

ius et al., 1999; Tarkiainen et al., 1999) of healthy adults indicate

that two ‘‘early’’ (<250 ms after stimulus presentation) activations

following visual presentation of words and word-like stimuli are

primarily involved in visual rather than in phonologic or semantic

word processing, although subtle effects of word class (Koenig and

Lehmann, 1996; Skrandies, 1998) are also reported. An activation

over the occipital scalp around 100 ms (P1/M100) is mainly

sensitive to physical stimulus characteristics (visual contrast,

luminance, size, etc.) and reflects activity of striate and extrastriate

visual areas (Dale et al., 2000; Dhond et al., 2001; Khateb et al.,

2002; Hauk and Pulvermuller, 2004a,b). The P1 is followed by an

occipito-temporal negative microstate around 140–220 ms (N1)

(Nobre et al., 1994; Bentin et al., 1999). In contrast to the P1, the

N1 microstate is category specific. It is the earliest component that

reliably distinguishes between word-like stimuli and visually

matched symbol strings (Schendan et al., 1998; Bentin et al.,

1999; Tarkiainen et al., 1999). The N1 amplitudes are larger to

S. Brem et al. / NeuroImage 29 (2006) 822–837 823

word-like stimuli (words, letter and pseudofont strings) than to

nonsense character strings (made from symbols, icons or other

forms), particularly over the left hemisphere (Schendan et al.,

1998; Bentin et al., 1999; Rossion et al., 2003; Brem et al., 2005;

Maurer et al., 2005a,b). The categorical specificity of the N1 to

word stimuli is also supported by its unique topography,

lateralization, and amplitude when compared with the N1 to faces

or objects (Schendan et al., 1998; Rossion et al., 2003).

Converging evidence from positron emission tomography (PET)

(Petersen et al., 1988; Petersen et al., 1990; Price et al., 1996;

Mechelli et al., 2000) and fMRI (Cohen et al., 2000; Dale et al.,

2000; Gaillard et al., 2000; Tagamets et al., 2000; Booth et al., 2001;

Dehaene et al., 2002; Schlaggar et al., 2002; Turkeltaub et al., 2003)

points to the crucial involvement of the occipito-temporal cortex in

extracting visual invariants of words and word-like stimuli. In

particular, an area in the left midfusiform gyrus (Talairach

coordinates (TC): �42, �54,�12) has been described as an

unimodal visual word form area (VWFA) activated automatically

for visually presented words (Cohen et al., 2000; Cohen et al., 2002;

Dehaene et al., 2002; Cohen and Dehaene, 2004). It is suggested to

be responsible for the computation of an abstract, ordered letter

identity representation from the visual input as a base for subsequent

higher level processing. Although most studies so far have observed

activation in the VWFA during visual word processing, some

studies indicate that this area is not exclusively activated by written

words, with activity being evoked by other types of visual stimuli

such as letter strings (Tagamets et al., 2000), falsefont strings (Price

et al., 1996; Tagamets et al., 2000; Turkeltaub et al., 2003), or even

pictures (Moore and Price, 1999) in the same area. These discrepant

results regarding the role of the VWFA have been explained by its

proximity to multimodal processing areas (Cohen and Dehaene,

2004) in lateral infero-temporal areas. Insufficient spatial resolution

of PET studies, strong smoothing of fMRI data, and interindividual

variability in the brain structures may contribute to the diverging

results. However, the discrepancies may also be due to the

insufficient temporal resolution of fMRI and PET data which are

not able to exclude contributions from later, sustained, or reentrant

activation of the same region (Dale et al., 2000). Nevertheless,

activity in the midfusiform gyrus of the language dominant left

hemisphere is thought to represent the metabolic correlate of some

aspects of the electrical N1. Support is provided by intracranial

neurophysiologic mappings of word- and face-specific ERPs

(Allison et al., 1994; Nobre et al., 1994; Puce et al., 1996), and

by noninvasive multimodal studies of word processing in the

same subjects (Cohen et al., 2000; Dale et al., 2000). Subsequent

higher level processing of visually presented words such as

grapheme–phoneme conversion or semantic access is carried out

predominantly in distinct left hemispheric brain structures of the

temporal and frontal lobes (Petersen and Fiez, 1993; Petersen et

al., 1988; Demonet et al., 1992, 1994, 2005; Paulesu et al., 1997;

Poldrack et al., 1999).

While the brain volume changes most dramatically in early

childhood, as indicated by structural and morphologic MRI studies

(Giedd, 2004), continued dynamic changes affect various sub-

components of the brain beyond adolescence. Structural and

morphologic changes in adolescence and early adulthood include

increases in gray matter volume, the amount of white matter, U-

shaped maturational curves (Giedd et al., 1999; Paus et al., 1999;

Giedd, 2004), and brain growth in regions involved in language

processing such as the dorsal frontal and occipito-temporal cortex

(Sowell et al., 2001). Ameliorations in connectivity due to synaptic

pruning are also recognized (Huttenlocher, 1979; Sowell et al.,

2001). These changes suggest continued functional development

such as accelerated neuronal processing. Following the prominent

changes in early childhood, and in parallel with late structural

maturation, late developmental changes of functional networks

during adolescence and early adulthood have been observed.

Neurophysiological ERP studies have provided clear evidence for

late development of visual word processing (Kok and Rooijakkers,

1985; Taylor, 1988; Holcomb et al., 1992; Taylor and Smith, 1995;

Grossi et al., 2001) in different tasks. These studies focused on

‘‘late’’ ERPs occurring after 250 ms, which reflect phonological

(Taylor, 1993; Grossi et al., 2001) or semantic (Taylor, 1988;

Holcomb et al., 1992) processing, rather than earlier steps such as

visual specialization for letter strings. Late activities such as the

frontal asymmetry following phonological primes, or the activity to

semantically incongruent sentence endings revealed major topo-

graphic changes beyond adolescence, consistent with continued

reorganization of language processing. Earlier visual P1 and N1

activities displayed amplitude or latency decreases with age

(Holcomb et al., 1992; Grossi et al., 2001) and paralleled similar

developmental changes for the later ERPs. Such changes are

referred to as nonspecific effects of maturation and automation in

this article since they are neither limited to a specific modality

(Holcomb et al., 1992) nor condition (Taylor et al., 1999). No late

development of the word N1 topography, which might reflect

specialization for words was reported (Holcomb et al., 1992;

Grossi et al., 2001), but such topographic changes may have gone

undetected due to insufficient analysis or coverage of posterior

temporal regions which are known to yield the largest word N1 in

adults (Nobre et al., 1994; Brandeis et al., 1995; Rossion et al.,

2003). Additionally, no control condition such as symbol strings

was used to distinguish between ongoing nonspecific ERP

maturation or specific progresses in word specialization.

Intriguingly, however, systematic developmental ERP mapping

studies focusing on visual specialization for faces have consistently

demonstrated that development and specialization of the early

visual P1 and N1 components continue beyond age 15 (Itier and

Taylor, 2004a,b; Taylor et al., 2004).

Developmental changes in visual specialization for words

during adolescence have also been examined in studies using

fMRI and PET (Petersson et al., 2000; Booth et al., 2001; Gaillard

et al., 2001; Schlaggar et al., 2002; Shaywitz et al., 2002;

Turkeltaub et al., 2003; Brown et al., 2005; Schapiro et al.,

2004). There is suggestive evidence from studies using both

implicit (Turkeltaub et al., 2003) and explicit language tasks

(Schlaggar et al., 2002; Shaywitz et al., 2002; Brown et al., 2005)

that lower level visual processing in posterior areas such as the

bilateral extrastriate cortices matures prior to higher language

processing in areas such as left parietal and frontal cortex. A

consistent developmental finding is the increase of left-anterior

activation with age, although both continued linear (Shaywitz et

al., 2002; Turkeltaub et al., 2003; Schapiro et al., 2004) and

asymptotic nonlinear (Brown et al., 2005) increases have been

found in adolescence. In addition to this posterior–anterior

maturation pattern, an attenuation of right hemispheric infero-

temporal activation (Turkeltaub et al., 2003) contributes to the

establishment of the typical left hemispheric dominance of the

language processing network. This activity decrease in posterior

regions reached adult-like levels at a slightly older age (¨15 years)

than developmental increases (<13 years) (Brown et al., 2005). Not

all developmental changes, however, follow the posterior–anterior

S. Brem et al. / NeuroImage 29 (2006) 822–837824

maturation pattern. Attenuations of activation with age noted in

sensory regions such as bilateral occipital and temporal cortex may

represent fine-tuning of lower-level mechanisms (Brown et al.,

2005).

Late development of visual specialization in the reading system

between adolescence and adulthood thus remains to be examined.

Even though the reading network seems to be established in

adolescence, minor alterations in the occipital and temporal cortex

detected after adolescence may represent protracted fine-tuning.

Moreover, no multimodal studies have examined developmental

changes using ERP and fMRI in the same subjects, and no

developmental ERP or fMRI studies have focused specifically on

late maturation of visual specialization in word processing. To

detect and understand the presumably subtle changes during late

development, it is indispensable to use imaging techniques that

allow the exact tracking of the time course as well as the precise

localization of areas involved in visual word processing. In our

study, we focus on late development of the early visual word

processing steps and implicit reading by comparing groups of

adolescents and adults performing a word and symbol string

repetition detection task using ERP and fMRI. We examine

whether the development of temporal (ERP) and spatial (fMRI)

aspects in the posterior visual word processing network is

completed prior to adolescence, or continues to display devel-

opmental changes.

We hypothesize that the topography and spatial organization of

the basic visual word processing network is fully established in

adolescence. However, since most adolescents continue training

and consolidating their reading skills, the degree and speed of

activation in the reading network should still change. N1 source

computations are expected to identify posterior brain areas

specialized for visual word processing, matching a subset of

posterior regions identified in the fMRI data. In accordance with

the literature, we anticipate that N1 latencies and amplitudes

decrease from adolescence to adulthood as words become

increasingly familiar and meaningful, but will not adapt for

unfamiliar and meaningless symbol strings.

Fig. 1. Schematic illustration of the task. In the implicit reading part,

subjects were instructed to press a button after immediate repetition of a

stimulus. Within the blocks of words and symbol strings, 20% null events

were pseudorandomly intermixed with the stimuli, resulting in more

variable ISIs. Only conditions analyzed in this paper are illustrated.

Materials and methods

Participants

18 adults (9 male, 9 female, aged 19.8–30.8 years, mean age

25.2 years, right handed) and 13 adolescents (5 male, 8 female,

aged 15.2–17.3, mean age 16.2 years, right handed) volunteered to

participate in this study. All had normal or corrected to normal

vision and none had a neurological or psychiatric disease, or

reading disorder. Their native language was Swiss–German. Most

of the adults answered on advertisements placed at universities.

Adolescents were contacted by distributing handouts at schools.

Each subject (plus one parent/caretaker of the adolescents) signed

informed consent. The study was approved by the local ethical

committee.

Procedure

All subjects took part in separate EEG and fMRI sessions. The

order of the sessions was counterbalanced and the average interval

between the two sessions was 9 days (3–34 days). In both

sessions, subjects had to perform the same four tasks (three visual,

one motoric). The order of the tasks was the same in both sessions

of a given subject but was counterbalanced across subjects. The

visual field (30-) for stimulus presentation and the visual angle of

stimuli (between 1.1- and 3-) were matched across the recording

settings. In this paper, we focus on the data of the visual word and

symbol string processing task. All subjects performed a short timed

pseudoword reading test, for which local adult norms were

available (item length 4, 6, 8, 10, and 12 letters, 5 pseudowords

of each length, totalling 120 syllables).

Stimuli and task

The implicit reading and symbol string processing task is

illustrated in Fig. 1. The task was to press a button after immediate

repetition of a stimulus. Words and symbol string stimuli were

presented either as black on a white background (no noise) or black

on a noisy (noise 34%) background. Words and symbol strings

were matched for character size and number of characters in a

string (3–5 characters). Symbol strings were slightly wider

(average visual angle: 2.2-, range: 1.3–3-) than words (average

visual angle: 1.9-; range: 1.1–2.4-) due to a difference in character

spacing. All words of both parts were concrete nouns chosen for

balanced familiarity on the basis of occurrence per million words

(average: 13; range: 0.5–237) in contemporary publications

(University of Leipzig: www.wortschatz.uni-leipzig.de). The first

letter of each word was capitalized, and the remaining letters

written in lowercase.

70 stimuli and 14 target stimuli of each condition were

presented in two blocks (35 + 7 stimuli). The stimuli of each

block (4 � 2) were pseudorandomly intermixed with 20% null

events (omitted stimuli, or no-stimulus events). The duration of

Fig. 2. ERP curves (GFP) to words (solid lines) and symbol strings (dashed

lines) in adolescents (black) and adults (gray). The vertical gray lines

indicate microstate borders (P1, early N1, late N1). Clearly visible are the

larger late N1 amplitudes in adolescents for both conditions, and the longer

N1 map latency in adolescents than in adults for the word condition only.

The distinction between words and symbol strings which starts in the early

N1 becomes most pronounced in the late N1 (GFP).

S. Brem et al. / NeuroImage 29 (2006) 822–837 825

each stimulus presentation was 700 ms. During the interstimulus-

interval (ISI) which varied following an exponential distribution

(500–650 ms), a centered fixation cross was shown either on white

background (no noise blocks) or noisy background (noise blocks).

Mean stimulus onset asynchrony (SOA) added up to 1240 ms for

both real stimuli and null events. Between each task block, 15 s

rest was introduced. In half of the rest blocks, a white screen with a

centered fixation cross was shown, in the other half of the breaks,

the fixation cross was projected on a noisy background.

The order of the blocks was counterbalanced across subjects.

Word and symbol string blocks with and without noise were

alternated. The main task was followed by two silent reading

blocks (35 stimuli + 7 target stimuli + 10.5 null events). All target

stimuli were used only to maintain subjects’ attention within the

recording sessions and were excluded from ERP and fMRI

analyses. To keep the focus on implicit reading and symbol string

processing, the results from the noise conditions and from the silent

reading part are not reported in this article.

Behavioral measures

All subjects performed satisfactorily (overall accuracy, includ-

ing targets and false alarms >89%, average overall accuracy words:

99.6%; and for symbol strings: 96.5%) for inclusion in subsequent

analyses. Mean reaction time to target stimuli and target accuracy

was further analyzed. Performance in the pseudoword reading task

(performed without ERP or fMRI recordings) was also analyzed,

and correlations with N1 mean amplitudes and activity in the left

midfusiform gyrus were computed.

Behavioral analyses

Target accuracy was transformed with arcsin to compute the

subsequent multivariate analyses of variance (MANOVAs). To

examine performance differences between age groups and compare

impact of method on behavior, we computed separate MANOVAs

for reaction time and target accuracy including the within-subject

factors condition (words, symbol strings) and method (EEG,

fMRI). To examine the influence of repeating the test, we also

computed MANOVAs with the within-subject factor session (first

vs. second session) rather than method.

ERP recording and processing

Participants were seated in an electrically shielded, video

monitored, soundproofed and air-conditioned recording room in

front of a computer display (distance 60 cm). Electrode positions

on the individual scalp were measured using a 3D digitizer. The

entire EEG session lasted about 2.5 h.

The ERPs were recorded from 63 channels with a sampling rate

of 500 Hz, filters set to 0.1–70 Hz, and calibrated technical zero

baselines. Fz served as recording reference, AFz as ground. Caps,

used for the montage, included all 10–20 system electrodes plus

the following additional electrodes: FPz, FCz, CPz, POz, Oz, Iz,

AF1/2, F5/6, FC1/2, FC3/4, FC5/6, FT7/8, FT9/10, C1/2, C5/6,

CP1/2, CP3/4, CP5/6, P5/6, TP7/8, TP9/10, PO1/2, PO9/10, Ol1/2,

and two EOG electrodes below the outer canthus of the each eye.

O1V/2V and Fp1V/2V were placed at 15% (5% more laterally) for

more even coverage. Ol1 and Ol2 were placed to the left and to the

right of the midline half-way between Oz and Iz for better occipital

coverage. The impedance for >80% of all electrodes was below 20

kV. The few electrodes with high impedances (<1.5% between 40

and 60 kV) nonetheless delivered reliable EEG signals.

Ocular artifacts were removed using an independent component

analysis (ICA) on 0.3–30 Hz filtered data, where necessary. The

ICA separates the input data into a sum of temporally independent

components of fixed topography and allows components domi-

nated by blink or lateral eye artifact distributions to be removed

without topographic distortion (Jung et al., 2001). Prior to

averaging, the corrected files were digitally bandpass filtered (1–

30 Hz, 24 dB), downsampled to 256 Hz, and epoched from �125

ms pre- to 1125 ms poststimulus. Artifacts exceeding T80 AV in

any channel were automatically rejected. Averaging was done

separately for each condition. ERPs were transformed to the

average reference (Lehmann and Skrandies, 1980) for all subse-

quent analyses. For each group (adults, adolescents), the averages

of the word and symbol string conditions were computed

separately and the resulting group average ERPs were normalized

to GFP = 1 (global field power, a time varying measure of map

amplitude computed as the spatial RMS; Lehmann and Skrandies,

1980) to ensure equal weighting. Time segments (see Fig. 2) of the

P1 (82–131 ms), the early N1 (132–182 ms), and the late N1

(183–256 ms) for use in all subsequent analyses were defined by

topographic microstate clustering (Pascual-Marqui et al., 1995) on

the grand mean of the normalized ERP files of adults and ado-

lescents including both conditions. Afterwards, the mean amplitude

values of the three microstate segments were computed for the

averaged GFP values of each timeframe within the segments. These

mean amplitude values were used for further statistical analyses.

Latency maps of the P1 and N1 (132–256 ms) microstates

were determined using a topographical component recognition

(TCR) (Brandeis et al., 1992), which uses a template map to

search for the topographically most similar map in each ERP.

For each condition separately, the grand mean of adults and

adolescents was calculated using the normalized grand average

ERP curves to exclude unequal weighting of the group maps. The

map at the P1/N1 GFP peaks of these two grand means were

S. Brem et al. / NeuroImage 29 (2006) 822–837826

chosen and used to determine the map latencies for each con-

dition and each subject individually in the P1/N1 microstates. In

order to avoid a topographical match with noise, times of high

GFP/low noise were favored by adding 5% GFP to the correla-

tion (rV = r + 0.05 * GFP) (Steger et al., 2000).

ERP statistics

Differences in GFP amplitudes between conditions and age

groups were tested using a multivariate analysis of variance

(MANOVA) for repeated measures with consideration of within-

subject factors: condition (words, symbol strings), segment (P1,

early N1, late N1), and of a between-subject factor: age (ado-

lescents, adults). This MANOVA was followed by separate

univariate ANOVAs for each segment.

Two separate MANOVAS (without the segment factor) were

performed to test differences in the peak latency of P1 and N1. Post

hoc t tests were used to explain significant interactions. Topo-

graphic bootstrapping tests (TANOVA; topographic analysis of

variance; Strik et al., 1998; Pascual-Marqui et al., 1999) on nor-

malized maps (GFP = 1) between adults and adolescents were

conducted for each condition separately to investigate differences

in scalp topography between the two groups in all three micro-

states. Topographic condition differences of normalized maps were

determined with TANOVAs in each group and each microstate.

TANOVA computes the exact probability of dissimilarity between

two maps (Lehmann and Skrandies, 1980) using bootstrapping

statistics, and protects against possible errors due to parametric

statistics or nonrepresentative summary measures. The TANOVA

on normalized maps (i.e. maps scaled to unity GFP) detects only

those purely topographic differences which cannot be explained by

overall amplitude (GFP) differences.

LORETA source localization

Low resolution electromagnetic tomography (LORETA;

Pascual-Marqui et al., 1994, 1999) was used to localize the

sources of the early and late N1 microstates. LORETA

determines the smoothest possible current source density

solution that accounts for the observed scalp EEG topography.

For the calculation of source solutions, potentials from EOG

electrodes were excluded as they can cause localization errors

because of nonuniform sampling of scalp potentials (Gottselig et

al., 2004). LORETA solutions were calculated for the early and

late N1 segments individually for each subject, for the word and

symbol string condition. Transformation matrices were calculated

by using the individually measured three-dimensional electrode

coordinates. The amount of over-smoothing was determined

objectively by using cross-validation within each individual ERP

file, resulting in different but optimal regularization parameters

for each file (Gottselig et al., 2004).

LORETA statistics

Paired statistical comparisons were conducted by using sta-

tistical nonparametric mapping, which corrects for multiple

comparisons at all 2394 voxels with t tests (Nichols and Holmes,

2002), to compare ERP sources and regions activated during visual

word processing in fMRI. To correlate ERP sources and fMRI

activity, a LORETA voxel of interest (referred to as VOI in this

article) in the midfusiform gyrus adjacent to the VWFA (TC: �52,

�46, �13) was chosen. For this VOI, the amount of LORETA

activation in late N1 for words was correlated (i) with word

activation in an fMRI region of interest (ROI) in the VWFA (ROI

3, see the Region of interest (ROI) analyses section) and (ii) with

reading performance measures. Comparison of word sources in

adolescents and adults was tested using statistical nonparametric

mapping.

fMRI recording and processing

fMRI was performed on a 1.5 T scanner (Intera, Philips Medical

Systems, Best, The Netherlands). Visual experiments were

projected on a screen located 2.08 m away from the subject’s

eyes. The participants viewed the screen via a mirror located just

above their eyes. Subjects were fitted with earplugs and invo-

luntary head movements were minimized using head padding. The

entire fMRI session lasted approximately 1.5 h. For functional

imaging, 28 axial slices covering the whole brain were acquired

with a blood oxygenation level dependency sensitive echo planar

imaging sequence (TR = 3 s, TA = 2.57 s, TE = 55 ms, matrix =

64 � 64; interleaved slice acquisition, slice thickness = 5 mm; no

interslice gap; flip angle = 90-, FOV = 220 � 220 mm) for a

total of 262 scans. The first 3 scans were discarded to allow for

equilibration effects. A high resolution (1 � 1 � 1 mm voxel

size, 100 slices) structural data set was acquired using a standard

3D T1-weighted FLASH sequence.

Data were preprocessed and analyzed using SPM2 analysis

(Wellcome Department of Cognitive Neurology, London, http://

www.fil.ion.ucl.ac.uk/spm). The data were first slice-scan-time

corrected by resampling the time course with linear interpolation so

that each voxel in a given volume represents the signal at the same

point in time. Afterwards, the data were motion corrected. No

individual run had more than half a voxel maximum displacement

in x-, y-, and z-plane. Images were normalized using a trilinear

interpolation method to match the anatomical images with the

Montreal Neurological Institute (MNI) template (Evans et al.,

1993). Functional volumes were spatially smoothed with a 9 mm

full width at half maximum isotropic Gaussian kernel.

The event-related activation due to each trial type (words and

symbol strings) was modeled with the standard SPM hemodynamic

response function. Events were contrasted against rest blocks

including noise, no noise breaks, and null events. Global scaling

was performed to remove global signal drifts across individual

time-series. In addition, a high-pass filter with a cut-off period of

320 s was used. Activated brain structures were identified by

transforming the MNI coordinate system of SPM2 into the

standard brain atlas of Talairach and Tournoux (Talairach and

Tournoux, 1988; Evans et al., 1993).

Statistical fMRI analyses

Condition and group analyses were conducted with random-

effect t tests using the individual contrast images smoothed at 9

mm. Statistical contrasts were made by using the averaged intensity

of voxels in the t statistic, and statistical parametric maps of t values

(SPM {t} maps) were generated. To minimize false positives and

false negatives, P values were corrected for multiple comparisons

using the family wise error (FWE) correction.

Areas activated by visual word or symbol string processing

were determined by contrasting each condition against baseline.

Clusters (k � 10) including voxels exceeding P < 0.01 were

S. Brem et al. / NeuroImage 29 (2006) 822–837 827

considered to show significant activations. Word- or symbol

string-specific activation was computed by the contrast words–

symbol strings and symbol strings–words, respectively. A P <

0.05 and a k � 10 were used to determine significantly activated

areas in these t tests. To investigate differences in the global

activation patterns between adults and adolescents, a minimal

cluster size of k � 10, and a false discovery rate (FDR) corrected

P < 0.1 indicated a trend (Genovese et al., 2002).

Region of interest (ROI) analyses

Five bilateral spherical (radius = 6 mm) ROIs were chosen

to examine the late maturation of basic and word-specific visual

processing areas more closely. The ROIs covered the putative

VWFA of the fusiform gyrus and adjacent areas along an

anterior–posterior axes (Fig. 3): ROI 1 (TC: T42, �30, �15),

ROI 2 (TC: T42, �41, �13), ROI 3 (=VFWA proper, TC: T42,�53, �12), ROI 4 (TC: T42, �67, �12), and ROI 5 (TC: T42,�78, �8). Specifically, ROI 3 represented the center of the

putative VWFA as described (Cohen et al., 2000) in the

midfusiform gyrus. The individual percent signal change values

in these ROIs were computed using MARSBAR v0.37 (Brett et

al., 2002) on unsmoothed data and used for statistical analyses.

As with the ERP analysis, a MANOVA with within-subject

factors: condition (words, symbol strings), hemisphere (left,

right), ROI (ROI 1, 2, 3, 4, 5), and the between-subject factor

age (adults, adolescents) was computed. Correlations of word-

specific activation in each ROI with N1 microstates were

computed to investigate the relation between activity in the left

fusiform gyrus and N1 in the whole sample. Additionally,

correlations between pseudoword reading performance (accuracy

and speed) and word-specific activity in the ROIs were assessed.

For all statistical analyses, only significant results (P < 0.05) or

trends of specific interest are reported. The main group compar-

isons were also computed using only a subgroup of older adults

(24.1–30.8 years, mean 26.6 years) matching the group of

adolescents in size (n = 13) to enlarge the age difference between

the groups. Results of these additional ‘‘analyses with increased

age separation’’ are reported only, when they differed regarding the

significance level from the main analyses.

Behavioral results

The overall accuracy (average: 98%) and accuracy to target

stimuli were high (average: 94%) in both conditions (average target

accuracy: words = 98.2%; symbol strings = 89.4%; percentage of

subjects with 100% target accuracy in both sessions for words (n =

19; 61.4%) and symbol strings (n = 2; 6.4%), respectively) and both

groups (average target accuracy: adults = 97.9%; adolescents =

87.4%). Accuracy and reaction time (average reaction times adults:

words = 526 ms, symbol strings = 536 ms; adolescents: words = 526

ms, symbol strings = 548 ms) did not differ between (i) groups

(adults and adolescents), (ii) sessions (EEG, fMRI), or (iii) whether

the subjects performed the task for the first or the second time

(influence of task repetition). Repeated words were detected more

accurately (F(1,29) = 72.46, P < 0.001) than repeated symbol

strings. The performance in the pseudoword reading task did not

differ between adults and adolescents (pseudoword reading speed in

syllables/minute: 185.5 adults; 187.6 adolescents). Accuracy and

reading speed were correlated in this task (r = 0.43, P < 0.05).

ERP results

Amplitudes of P1 and N1

The multivariate GFP analysis including all three segments (P1,

early N1, late N1) and both conditions (words, symbol strings)

revealed a main effect of age (F(1,29) = 9.9 P < 0.01), an

interaction for segment and condition (F(2,28) = 7.84, P < 0.01),

and a segment � condition � age interaction (F(2,28) = 7.34, P <

0.01). Curves of GFP are illustrated in Fig. 2, the corresponding

maps of the N1 microstates in Fig. 4.

P1 (82–131 ms): Analyses of GFP amplitudes in this segment

revealed a condition main effect (GFP F(1,29) = 5.44, P < 0.05)

indicating larger P1 amplitudes for symbol strings in the GFP.

When tested with increased age separation, this condition main

effect was replaced by an age main effect (F(1,24) = 4.39, P <

0.05) indicating higher GFP amplitudes for adolescents.

Early N1 (132–182 ms): No significant effects of age or

condition were found in the GFP.

Late N1 (183–256 ms): The GFP indicated that the late N1

(Figs. 2–4) was more pronounced to words than to symbol strings

(GFP F(1,29) = 11.02, P < 0.01). The late N1 was larger for

adolescents than for adults (GFP F(1,29) = 15.77, P < 0.001). The

interaction of condition and age (GFP F(1,29) = 9.63, P < 0.01)

finally revealed that the difference between the conditions (words,

symbol strings) was more pronounced in adolescents than in

adults, thereby showing condition-specific age effects.

Map latency

Neither P1 nor N1 latencies showed significant main effects of

condition. A main effect of age (F(1,24) = 5.45, P < 0.05), showing

slower latencies for adolescents than adults, was only detected in the N1

latency range by increasing the age separation of the groups.A significant

interaction of condition and age for the N1 latency (F(1,29) = 6.14, P <

0.05; increased age separation: F(1,24) = 8.17, P < 0.01) indicated that

age had a differential effect on words and symbol strings. Post hoc t tests

revealed that, with age, the N1 latency became significantly faster for

words (P < 0.05) but not for symbol strings.

Topographical analyses (TANOVA)

While the word- and symbol string-related scalp topographies

differed in both groups in both N1 microstates (adults and

adolescents both: early N1, P < 0.001, late N1: P < 0.001), only

the P1 microstate of adults differed topographically between the

conditions (P < 0.05). The scalp topography (Fig. 3) of the P1

changed with age for symbol strings (P < 0.05) but not for words.

No topographic differences were found in the early N1 segment,

but the late N1 again showed topographic differences (P < 0.05 for

symbol strings) between adults and adolescents. An overview over

changing map topographies and strengths 50–270 ms following

stimulus presentation is given by Fig. 3.

N1 source localization with LORETA

LORETA sources (P < 0.01) for word-specific activations

(words–symbol strings) of the early and late N1 are illustrated

in Fig. 4 for each group separately. The overall maximum of

the word-specific early N1 in adolescents was located in the left

posterior middle temporal gyrus (BA37: TC �52, �60, 1) and

Fig. 3. Map series during word and symbol string processing 55–270 ms following stimulus presentation. The four top rows illustrate map topographies (AV) forboth groups and each condition separately. Rows 5 and 6 depict topographic differences between words and symbol strings in the adult and adolescent groups

(colored t values correspond to a P < 0.01 in both groups) which are most pronounced in the N1 interval. The bottom rows illustrate the subtle topographical

differences between the age groups for either word or symbol string processing (colored t values correspond to a P < 0.01 in both conditions). This illustration

also clarifies that the differences between conditions which start at the end of the P1 segment may be attributed to the beginning of the N1 effect.

S. Brem et al. / NeuroImage 29 (2006) 822–837828

extended to the superior temporal gyrus. An additional

maximum was found in the right hemispheric posterior middle

temporal gyrus. No region in the late N1 of adolescents reached

a significance level of P < 0.01. Less significant sources (P <

0.05), however, were found in the left fusiform gyrus (BA 37),

left insula (BA 13), and bilateral medial frontal gyri (BA 10).

The focus of activation in the word-specific early N1 of adults

was also localized in the left superior temporal gyrus (BA 39:

TC 52, �53, 8). This source was blurred over regions adjacent

to the temporal–parietal–occipital junction in the left hemi-

sphere such as middle temporal gyrus, inferior temporal gyrus,

superior occipital gyrus, middle occipital gyrus, angular gyrus,

cuneus, and superior parietal lobule. For the adults’ late N1, the

word-specific global maximum extended from the left para-

hippocampal gyrus (BA 36: TC �38, �25, �13) to fusiform,

infero-temporal, and middle-temporal gyri.

fMRI results

To investigate the neural systems involved in visual word or

symbol string processing, the brain activity for each stimulus type

Fig. 4. Maps of the early and late N1 for words (first row), symbol strings (second row), and topographical differences between the two conditions (words–

symbol strings, scaled to GFP = 1; third row). Maps of adolescents are illustrated on the left side, maps of adults on the right side. Reduced negativity to symbol

strings at left occipito-temporal electrode sites starts in the early N1 for both groups. LORETA (bottom row) of the corrected, paired statistical comparisons of

words and symbol strings. While the word-specific early N1 sources extended over the temporal–parietal–occipital junction in the left hemisphere, the late N1

sources were focused near the left fusiform gyrus.

S. Brem et al. / NeuroImage 29 (2006) 822–837 829

(words, symbol strings) was contrasted against baseline (Table 1).

Areas activated by either the word or the symbol string condition

as well as word- (words–symbols) and symbol string- (symbol

strings–words) specific activations are illustrated in Fig. 6 and

summarized in Table 2.

Comparing adults and adolescents

No significant differences between adults and adolescents were

found regarding their global activation pattern. This held for words,

symbol strings, and their differences. Only one area in the right

superior temporal gyrus (BA 22: TC 56, 5, 9; P < 0.1 (FDR

corrected)) tended to be more active in adults than in adolescents

for words. The activation tables (1 + 2) and Fig. 6 thus show

pooled activation over all 31 subjects.

Word activation

Consistent with previous studies (Petersen et al., 1990; Price

et al., 1996; Cohen et al., 2000; Dehaene et al., 2002;

Turkeltaub et al., 2003; Kronbichler et al., 2004), the activation

foci for words vs. baseline (Table 1) were found in left

posterior temporal areas extending from the fusiform gyrus to

the middle and inferior occipital gyri. Activation in the frontal

lobe showed maxima in the left superior, middle, and inferior

frontal gyri. Two maxima were in the left middle temporal

gyrus. In the right hemisphere, words activated the cingulate

gyrus, the superior temporal gyrus, the inferior frontal gyrus,

and middle and inferior occipital gyri. Word-specific activation

foci were determined by contrasting words with symbol strings

(Table 2). Such word-specific activations were only found in

the left hemisphere and included the inferior and superior

frontal gyri, the middle and superior temporal gyri, and the

precuneus.

Symbol string activation

We found a more bilateral activation pattern for symbol

strings compared to words (Table 1) as already described in

previous studies (Price et al., 1996; Tagamets et al., 2000). The

overall posterior maximum included the bilateral middle and

inferior occipital and inferior temporal gyri. Other maxima

specific for symbol string processing were found in left and

right inferior parietal lobule extending into the superior parietal

lobule/precuneus, respectively. The frontal lobe maxima

included the left superior and medial frontal gyri, bilateral

middle frontal gyri, and the right inferior frontal gyrus. When

contrasting symbol strings to words (Table 2), the precuneus,

Table 1

Activations for words (left) and symbol strings (right) vs. baseline at P < 0.01 (FWE corrected), k � 10, pooled over all 31 subjects

Words vs. baseline Symbol strings vs. baseline

Cluster size t value Z score x y z Location BA Cluster size t value Z score x y z Location BA

Left hemisphere

590 13.4 7.6 �42 �62 �7 FFG 37 671 16.4 >8 �48 �79 1 MOG 19

11.3 7 �45 �47 �8 FFG 37 15.1 >8 �42 �70 �7 IOG 19

10.8 6.9 �42 �73 �4 IOG 19 11.7 7.1 �39 �87 �12 IOG 18

69 10.0 6.6 �3 11 55 SFG 6 351 10.1 6.6 �33 �50 55 IPL 40

8.4 6 �6 3 58 MFG 6 9.94 6.6 �42 �38 49 IPL 40

354 9.8 6.5 �45 26 �14 IFG 47 9.8 6.5 �27 �56 50 PreCu 7

9.5 6.4 �50 29 �6 IFG 47 118 10.0 6.6 �3 14 49 SFG 6

9.4 6.4 �53 21 7 IFG 45 7.9 5.8 �6 3 61 MeFG 6

17 7.7 5.7 �56 �24 �9 MTG 21 18 8.1 5.8 �24 �68 31 PreCu 7

7.4 5.6 �56 �18 �14 MTG 21 23 7.4 5.5 �48 7 33 MFG 9

Right hemisphere

63 8.3 5.9 21 �34 24 CG 31 601 14.6 7.8 45 �67 �2 ITG 37

28 7.9 5.8 42 �38 7 STG 41 14.3 7.8 39 �87 7 MOG 19

12 7.8 5.7 48 4 27 IFG 9 12.9 7.4 42 �76 �4 IOG 19

36 7.8 5.7 39 �79 �4 IOG 19 366 10.9 6.9 48 �33 46 IPL 40

7.7 5.7 39 �90 2 MOG 18 9.4 6.4 33 �44 46 IPL 40

10 7.7 5.7 45 �67 3 MOG 37 9.1 6.3 30 �53 41 SPL 7

87 9.2 6.3 53 10 33 IFG 9

10 8.0 5.8 42 36 23 MFG 46

26 7.8 5.7 30 2 50 MFG 6

Abbreviations: x, y, z in Talairach coordinates; BA, Brodman Area; SFG, Superior frontal gyrus; MFG, Middle frontal gyrus; MeFG, Medial frontal

gyrus; IFG, Inferior frontal gyrus; STG, Superior temporal gyrus; MTG, Middle temporal gyrus; ITG, Inferior temporal gyrus; SPL, Superior parietal

lobule; IPL, Inferior parietal lobule; MOG, Middle occipital gyrus; IOG, Inferior occipital gyrus; PreCu, Precuneus; FFG, Fusiform gyrus; CG, Cingulate

gyrus.

S. Brem et al. / NeuroImage 29 (2006) 822–837830

bilateral superior and inferior parietal lobules, the right

postcentral gyrus, the bilateral middle occipital gyri including

parts of the middle temporal, and the left fusiform gyrus

exhibited more pronounced activity.

Table 2

Activations for the contrasts words–symbol strings (left) and symbol strings–wor

Words–symbol strings S

Cluster size t value Z score x y z Location BA C

Left hemisphere

97 9.6 6.5 �42 25 �16 IFG 47 3

8.7 6.1 �53 24 4 IFG 45

7.5 5.6 �50 32 1 IFG 47

200 9.4 6.4 �53 �46 5 MTG 22 1

6.0 4.9 �56 �27 �9 MTG 21

9.1 6.3 �48 �60 28 STG 39

8.2 5.9 �42 �71 42 PreCu 19

20 6.9 5.3 �18 31 45 SFG 8

6.4 5.0 �15 40 45 SFG 8

Right hemisphere

2

1

9

1

Abbreviations: x, y, z in Talairach coordinates; BA, Brodman Area; SFG, Superio

MTG, Middle temporal gyrus; SPL, Superior parietal lobule; IPL, Inferior parietal

gyrus; FFG, Fusiform gyrus.

Region of interest analyses

The MANOVA of the five ROIs revealed no significant

multivariate age-related differences. However, analyses with

ds (right) at P < 0.05 (FWe corrected), k � 10, pooled over all 31 subjects

ymbol strings–words

luster size t value Z score x y z Location BA

07 10.0 6.6 �9 �73 56 PreCu 7

9.2 6.3 �18 �64 53 SPL 7

9.1 6.3 �45 �38 54 IPL 40

30 10.0 6.6 �33 �84 12 MOG 18

7.8 5.7 �48 �75 12 MTG 39

21 6.7 5.2 �33 �65 �17 FFG 19

23 6.7 5.1 �45 �67 �7 MOG 37

93 12.0 7.2 18 �67 53 SPL 7

8.0 5.8 27 �68 39 PreCu 7

06 9.0 6.2 48 �33 49 PoCG 40

8.0 5.8 39 �38 52 IPL 40

7 8.9 6.2 36 �78 23 MTG 19

8.5 6.0 36 �81 10 MOG 19

7 7.0 5.3 48 �59 �5 MOG 19

r frontal gyrus; IFG, Inferior frontal gyrus; STG, Superior temporal gyrus;

lobule; MOG, Middle occipital gyrus; PreCu, Precuneus; PoCG, Postcentral

Fig. 5. Percent signal change in each of the five bilateral ROIs in the occipito-temporal cortex, located on an anterior–posterior axis. On the top left hand corner

of the right chart, the projection of the 5 ROIs on a single subject’s T1 image is illustrated. ROI 1 (dark gray) was located most anterior, ROI 5 (light gray) most

posterior in the occipito-temporal cortex. Words (black) and symbol strings (gray) are illustrated separately. ROI 3 corresponded to the center of the VWFA

described in previous studies.

S. Brem et al. / NeuroImage 29 (2006) 822–837 831

increased age separation showed an age main effect (F(1,24) =

9.82, P < 0.01), indicating higher signal changes in adolescents

than adults. Overall magnitude of activation in the ROIs was larger

for symbol strings than for words (F(1,29) = 17.97, P < 0.001).

The ROIs differed considerably in their activation (F(4,26) =

24.68, P < 0.001), with more posterior ROIs having larger signal

changes than anterior ROIs (Fig. 5). The amount of differential

signal change was also influenced by the location of the ROIs on

the anterior–posterior axes (condition � ROI: F(4,26) = 13.1, P <

0.001). Post hoc t tests revealed that ROIs located anterior in the

left occipito-temporal gyrus showed an enhanced signal change for

words (ROIs 1 + 2 both P < 0.05), while the opposite was observed

Fig. 6. fMRI data of words and symbol strings pooled over all 31 subjects.

Condition vs. baseline effects (top) shown for FWE corrected random effect

analyses ( P < 0.01, k � 10). Paired t tests between words and symbol

strings (bottom row) illustrate areas specifically activated for each condition

separately (FWE corrected P < 0.05, k � 10).

for the two posterior ROIs (ROI 4: P < 0.001; ROI 5: P < 0.01).

No difference between the signal change of words and symbol

strings was detected in the center of the VFWA (ROI 3). In the

right hemisphere, the middle and posterior ROIs exhibited higher

signal change for symbol strings than words (ROI 3: P < 0.01, ROI

4: P < 0.001, ROI 5: P < 0.05).

Correlation of N1 amplitudes and fMRI activations

Comparing ERP and fMRI data, word activity in the ROI of the

VWFA proper correlated significantly with GFP map amplitude in

the late N1 segment (late N1 r = 0.41, P < 0.05, see Fig. 7A).

Correlation of reading performance with N1 amplitudes LORETA

VOI and fMRI activation

As illustrated in Fig. 7B, reading accuracy but not reading

speed correlated negatively with the word-specific late N1

amplitude (difference late N1: words–symbol strings; for GFP

r = �0.39, P < 0.05). The correlation of pseudoword reading

accuracy and VOI activity (r = �0.36, P < 0.05) (Fig. 7D)

furthermore indicated that lower reading scores were related to

diminished activity in the late N1 microstate.

Percent signal change in ROI 2 of the word activity correlated

significantly with reading speed in the pseudoword reading test

(r = �0.44, P < 0.05 Fig. 7C), indicating that slow reading was

associated with low signal change in ROI 2.

Discussion

This multimodal study focused on late development of visual

word processing by using ERP, fMRI, and performance measures

and investigated whether specialization of the reading system

continues to develop from adolescence into adulthood. In keeping

with our predictions, the reading network in adolescents was found

to be already well established and functionally equivalent to the

reading network of adults in typical tests. These results were

confirmed by the equal reading accuracy and speed of both groups

in our pseudoword reading test and are consistent with normative

data. The reading test results were paralleled by the performance in

Fig. 7. Selected correlations between fMRI activity, LORETAVOI activity, ERP amplitude, and pseudoword reading performance across both adults (black

circles) and adolescents (white circles). (A) Positive correlation of the % signal change in ROI 3 (VWFA) with the late N1 GFP amplitude (r = 0.41) for words.

(B) Negative correlation of the word-specific (words–symbol strings) late N1 GFP amplitude with pseudoword reading accuracy (r = �0.39). (C) Negative

correlation of % signal change in ROI 2 (anterior to the VWFA proper) and pseudoword reading speed (r = �0.44). (D) Negative correlation of VOI activity in

the late N1 for word and pseudoword reading accuracy (r = �0.36).

S. Brem et al. / NeuroImage 29 (2006) 822–837832

the visual word and symbol string processing repetition detection

task which likewise revealed no differences in accuracy and

reaction time between adolescents and adults. We therefore

conclude that the task difficulty for both age groups was

comparable. Neither the recording session (fMRI or EEG) nor

task repetition affected behavioral measures, indicating that no

learning effects over sessions occurred, and that the different test

environments did not affect performance.

ERP

Consistent with previous studies comparing words and visually

matched symbol strings (Schendan et al., 1998; Bentin et al., 1999;

Tarkiainen et al., 1999; Brem et al., 2005), both stimulus types

evoked an occipital positive P1 followed by an occipito-temporal

negative N1 microstate. Symbol strings elicited a P1 of similar

latency but larger amplitude than words. Such early condition

effects may reflect language-specific processing (Skrandies, 1998;

Ortigue et al., 2004) but also increased attention due to more

detailed local feature analysis for symbol strings as compared to

words (Han et al., 2000). The topographic similarity of the present

P1 effect with the subsequent N1 effect, and its emergence near the

end of the P1 microstate, however, suggest that it rather reflects a

beginning N1 effect (Fig. 3). The expected categorical specificity

(Allison et al., 1994; Bentin et al., 1999; Rossion et al., 2003; Brem

et al., 2005; Maurer et al., 2005a) of the N1 microstate was

reflected by the higher amplitudes and the different topographies

for words than symbol strings.

In sharp contrast to the absence of age effects at the performance

level, ERP analyses of the visual word processing task revealed

considerable differences between adolescents and adults in the

selected time ranges. In particular, whereas the topography of the P1

and the early N1 was similar across groups (Fig. 3), topographic

differences were found for the late N1 for symbol strings. This

topographic development reflects subtle and transient differences in

the distribution of activity within a subset of the network generating

the late N1. However, no topographic development related to

specialized word processing was evident, as the topographic effects

did not extend to the difference maps. Further, amplitudes were

generally larger for adolescents than adults, with a reliable

difference for the late N1 microstate. Such amplitude differences

could be observed even earlier in the P1 microstate when increasing

the age difference between the groups. These age effects on

amplitudes may, in part, reflect nonspecific maturation, since they

S. Brem et al. / NeuroImage 29 (2006) 822–837 833

were similar for both conditions (P1), or were larger for the

condition with stronger activity (late N1). Such maturational

changes involving constant or proportional activity reductions

may be due to morphological or structural alterations of the

underlying brain regions, such as a decline in the number of

synapses. Accordingly, the P1, and particularly the late N1

microstate sources may include regions with protracted morpho-

logical maturation. Physical accounts such as increases in the

thickness or attenuation in the conductivity of the skull cannot

explain these results, as they should have affected all microstates to

a similar degree. The more pronounced developmental reduction of

the late N1 for words than symbol strings may also reflect

automation of visual word processing, considering that the N1 also

increases with discrimination demands (Vogel and Luck, 2000).

This account assumes that the categorization process needs less

attentional resources in adults than in younger subjects, probably

due to a higher grade of automation and familiarity to words.

Although simple decreasing attentional demand as a result of

reading practice and age is a likely factor, it is also arguable that

clear categorization within 200 ms should be a prerequisite for the

quality and speed of subsequent word processing and accurate

reading. This latter mechanism would favor the more pronounced

word–symbol string differences we found in the late N1, which

were associated with better reading skills, as determined by the

correlation with pseudoword reading accuracy.

Our data agree with other studies showing that development in

the late teens affects early visual processing as reflected by the P1

and the N1. Developmental studies on visual word processing

(Holcomb et al., 1992; Grossi et al., 2001) have previously reported

that P1 and N1 amplitudes diminish with age, but did not test

specifically for continued changes from adolescence to adulthood.

Such late amplitude decreases have so far only been reported for the

P1 to faces, particularly in implicit tasks (Taylor et al., 2004). While

the N170 to faces also exhibits late development, it follows a

different pattern, with prominent topographic changes preceding an

increase of occipito-temporal amplitudes after age 15 (Itier and

Taylor, 2004a,b; Taylor et al., 2004). The N1 latency changes found

herein likewise point to late development, but reveal word-specific

development. Decreasing latencies for words but not for symbol

strings were found when comparing adults and adolescents,

indicating that only specialized categorical visual word processing

on the level of the N1 continues to progress from adolescence to

adulthood. When increasing the groups’ age separation, latencies

decreased for both conditions. Still, the decrease for word latencies

remained more pronounced, and confirmed the difference in

development between the conditions. Such latency decreases of

early ERPs in development are consistent with literature on visual

processing of verbal stimuli (Holcomb et al., 1992; Grossi et al.,

2001). Postadolescent latency alterations of such low level visual

processing mechanisms have rarely been tested. Evidence for

decreasing latencies beyond childhood comes mainly from studies

investigating face development. While P1 latency accelerations

(Itier and Taylor, 2004a,b) continued until adulthood, N170

latencies reached their adult levels earlier, around age 14 (Taylor

et al., 2001, 2004). Decreasing latencies characterize maturation,

and have usually been associated with enhanced processing speed

in existing networks. Faster processing may partly be due to faster

neural transmission, more efficient connectivity, and other matura-

tional processes in the underlying networks.

The discrepancy between decreasing word N1 latencies and

less affected symbol string latencies may reflect activation of

word-specific regions with protracted maturation. Specific struc-

tural alterations beyond adolescence, such as ongoing myelina-

tion of fiber tracts and reductions in gray matter density

associated with growth (Sowell et al., 2001), have indeed been

observed in posterior temporal areas coincident with our N1

source localization. These may allow increased efficiency and

functionality in the extrastriate brain areas involved in visual

stimulus categorization and tuning for print. The discrepancy may

also be due to more cognitive factors such as the continued

practice with words but not with symbol strings due to reading.

While words may be processed faster and with less effort through

reading practice, symbol strings remain unfamiliar and more

attention demanding. We assume that ongoing explicit daily

reading training during education or implicit training through

confrontation with the immense amount of written material in

daily life drives an increase in the automation of early stage

visual categorization processes in the reading network, as

demonstrated by our ERP data. The two explanations are by

no means mutually exclusive, considering recent reports of

increasing gray matter density in posterior temporal regions after

practice (Draganski et al., 2004).

The postadolescent development observed for the P1 and the

N1 was not, however, accompanied by better performance of the

adults in either the implicit or the explicit reading tests. One may

assume that the acceleration of early processing stages allows

adults to process more information at the same time, since this time

advantage is not used for faster word processing. In that case,

adults would perform better in a multitasking experiment. Whether

the present tasks were too easy to assess subtle performance

differences between the groups, or whether reduced attentional

demand, automation of task performance, or still other aspects of

performance not tested in this study are reflected by the ERPs, thus

has to be investigated in future studies.

The different LORETA sources for the early and late part of the

N1 illustrate the progression of activation due to implicit reading

within the N1 time range in adults and adolescents (Fig. 4).

Sources for the contrast of words and symbol strings were detected

mainly in basal occipito-temporal and lateral temporal areas in

both groups in correspondence to previous localizations of N1

sources (Puce et al., 1996; Dale et al., 2000; Khateb et al., 2002).

The strongest early N1 source in both groups was located in

posterior parts of the left middle and superior temporal gyri. Only

adults’ posterior activity included low level visual processing areas

such as the striate and extrastriate cortices. The proximity of the

adolescents’ maximum to the temporal–parietal–occipital junc-

tion, however, indicated that the same process of basic visual

feature analysis was represented by the early N1 in the younger

group as well. A common ventro-temporal source of the late N1

which has been suggested to represent stimulus categorization

(Batty and Taylor, 2002) and visual tuning for print (Nobre et al.,

1994; Dehaene, 1995; Puce et al., 1996; Dale et al., 2000;

Proverbio et al., 2004; Brem et al., 2005) common to both groups

included the fusiform gyrus. These findings are in agreement with

the MEG-based source localization of visual feature analysis in

areas of the striate cortex and subsequent spreading of activation to

left occipito-temporal regions (Tarkiainen et al., 1999; Dale et al.,

2000; Salmelin et al., 2000). They also match intracranial (Allison

et al., 1994) recordings of the maximal word N1 in the occipito-

temporal cortex and fMRI-constrained results on visual word

processing (Puce et al., 1996; Dale et al., 2000) in the left ventral

occipito-temporal cortex.

S. Brem et al. / NeuroImage 29 (2006) 822–837834

fMRI

Both the common and the differential activation of words and

symbol strings followed our expectations: While word presenta-

tions evoked a more left lateralized activation pattern (Price et al.,

1996; Cohen et al., 2000; Tagamets et al., 2000; Gaillard et al.,

2001; Holland et al., 2001; Schlaggar et al., 2002; Gaillard et al.,

2003; Turkeltaub et al., 2003), the corresponding activation pattern

for symbol strings was more symmetrically distributed over both

hemispheres (Price et al., 1996; Tagamets et al., 2000). Consistent

with previous studies of visual word and false font processing

(Price et al., 1996; Tagamets et al., 2000; Turkeltaub et al., 2003),

the activated network comprised striate and extrastriate areas in the

posterior occipital and temporal cortex including the VWFA of the

left fusiform gyrus. The maxima of activation in the brain volume

for words were found in the left hemispheric VFWA of the

fusiform gyrus. The main focus of activation for symbol strings

was localized close to the posterior focus of word-related activation

in the middle and inferior occipital gyri.

The direct contrasts revealed multiple areas specifically involved

either in word or symbol string processing. Words triggered implicit

reading even though no linguistic decisions were required to perform

the task (Price et al., 1996; Tagamets et al., 2000; Turkeltaub et al.,

2003), as shown by the involvement of higher level language areas

only for words. In contrast, symbol strings evoked more activity in

the extrastriate areas of the occipital lobe implicated in low level

visual feature analysis and in parietal regions associated with

enhanced working memory involvement (Becker and Morris, 1999;

Becker et al., 1999; Turkeltaub et al., 2003). These results parallel

the performance advantage for words found in our task and

correspond to previous findings (Tagamets et al., 2000).

The networks activated in the brain during implicit reading or

symbol string processing were the same in adults and adolescents,

though the foci of activation in the clusters were not always at

exactly the same location. Direct comparisons revealed only

nonsignificant differences between the two groups outside the

typical reading network.

Systematic investigation revealed a posterior–anterior gradient

of increasing word specificity in the left hemisphere. The ROIs

anterior to the putative VWFA revealed more signal change for

words compared to symbol strings. In contrast, the posterior ROIs

were more activated by symbol strings than by words while the

VWFA proper did not distinguish between the two conditions in

line with previous studies (Price et al., 1996; Tagamets et al.,

2000). In most other studies reporting increased activity for words

in the VWFA proper, words were compared with either pictures

(Gauthier et al., 2000; Hasson et al., 2002) or checkerboards

(Cohen et al., 2000), which differ in several important visual

aspects from letter strings. Similarly to previous studies (Cohen

and Dehaene, 2004; Cohen et al., 2000, 2002; Dehaene et al.,

2002; McCandliss et al., 2003; Kronbichler et al., 2004), also in

our task, words activated a left posterior occipito-temporal network

in adults and adolescents including an activation maximum within

the VWFA. However, we found that these areas with maximal

activation were neither exclusively activated for words, nor reliably

distinguished between words and symbol strings, as previously

reported (Tagamets et al., 2000; Turkeltaub et al., 2003). Cohen et

al. (2002) have argued that the higher demands of symbol-string

processing may induce a widespread increase of visual activation

masking a preferential VWFA activation for words. The left

anterior–posterior gradient of word-specificity suggests that

increasing symbol–string processing demands should also lead

to an apparent anterior shift of word-specific processing, while

using less demanding control stimuli such as checkerboards should

have the opposite effect.

Contrary to our expectations based on the age effects of the late

N1, no word-specific age effects were observed in any of the

occipito-temporal ROIs, and nonspecific age effects were only

detected after increasing the age separation of the groups. The

higher signal changes in the younger subjects, independent of

condition and location of ROI within the occipito-temporal cortex

pointed to protracted development. This decline in the amount of

signal change parallels the N1 amplitude decreases with its basal

occipito-temporal lobe sources, and replicates the finding that age-

related decreases in activity occur mainly in posterior brain regions

(Brown et al., 2005). Direct support for the relation of ERP activity

in the N1 time range and fMRI activity in the VWFAwas provided

by their correlation. Similarly to ERP measures, the signal change

in a partly word-specific ROI anterior to the VWFA correlated with

reading performance. We therefore conclude that ERP N1 and

fMRI activation in the VWFA represents different measures of the

same process. The lack of word-specific changes with age in the

local fMRI analysis also supports our conclusion that the spatial

distribution of the reading network is fully established in

adolescence.

Conclusion

In summary, late development of the visual word processing

systemwas investigated using ERP and fMRI with a visual word and

symbol string processing task. Data analyses mainly focused on the

P1 and N1 ERP microstates, the global activation pattern of adults

and adolescents, and on functional activity in specific parts of the

occipito-temporal cortex. Commonalities as well as discrepancies

regarding late development and condition effects were observed

when comparing fMRI and ERP results. The results of both

techniques indicated that the topography of specialized word

processing, as reflected by the differential fMRI activations and

the ERP difference map topographies, is already fully established in

the adolescents. They also converged regarding the enhanced

occipital activation and increased P1 amplitude to symbol strings.

Both techniques thus suggest more intensive low level feature

processing in symbol strings compared to words. ERP and fMRI

furthermore clearly distinguished between word and symbol string

processing either in strength or scalp topography of the P1 and N1

microstates or in the global metabolic activation pattern. Focused

analyses of the lateral occipito-temporal gyrus revealed an anterior–

posterior gradient of decreasing activation specifically to words and

an increasing activation to symbol strings. This finding is in

agreement with the localization of the word-specific late N1 close

to the VFWA and confirms the close relationship between word N1

and metabolic activity in the VFWA. Behavioral performance and

reading accuracy were comparable in adults and adolescents, in line

with our fMRI data and with the difference in ERPs. Despite this,

strong ERP evidence for late development of the activation

dynamics within the reading network indicated both condition

nonspecific maturational changes and condition-specific age effects.

Maturational changes were implicated by the consistent reduction of

late N1 activity to both words and symbol strings, and was paralleled

by decreased signal changes in occipito-temporal areas in adults.

The lack of such reductions in the early N1 indicates a high temporal

S. Brem et al. / NeuroImage 29 (2006) 822–837 835

specificity of this developmental effect to neural activity within

occipito-temporal areas as reflected by late N1 sources. Protracted

morphologic and structural maturation may contribute to these

nonspecific age effects, as proportional amplitude reductions are

consistent with decreases in synaptic density or connectivity. The

high temporal specificity of the developmental ERP effects may

explain why only subtle developmental changes in the fMRI data

were observed.

N1 latency decreases only for words represent a condition-

specific age effect. It identifies postadolescent development within

the specialized visual word processing network by the LORETA

source analysis. Shorter latencies may reflect faster signal trans-

mission due to ongoing myelination at the neural level, and more

proficient and faster word processing at the cognitive level. The

finding suggests that stimulus categorization and word recognition

become more efficient and automatic with age and reading

experience. They also indicate that neural markers of specialized

visual word processing at the N1 level reflect the quality of reading.

In conclusion, this study contributes to the understanding of

developmental processes in the reading system. While confirming

the hypothesis that the visual word processing network is already

established in adolescence regarding its spatial organization, we

also show that within this network further development takes place

from adolescence to adulthood.

Acknowledgments

Special thanks go to the group of Prof. Dr. P. Bosiger at the

Institute for Biomedical Engineering, University and ETH Zurich

and to the group of Dr. S. S. Kollias at the Institute of Neu-

roradiology, University Hospital Zurich, for providing fMRI

scanner time and for their kind support during fMRI recordings.

The authors also gratefully thank Dr. Th. Koenig who provided the

ICA code for the Brain Vision Analyser software. Finally, sincere

thanks to all our subjects and their families for their participation.

This study was funded by the NCCR on Neural Plasticity and

Repair.

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