Developmental neural networks in children performing a Categorical N-Back Task

www.elsevier.com/locate/ynimg NeuroImage 33 (2006) 980 – 990

Developmental neural networks in children performing a Categorical N-Back Task Kristina T. Ciesielski, a,b,⁎ Paul G. Lesnik, b Robert L. Savoy, a Ellen P. Grant, a,c and Seppo P. Ahlfors a a

MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, 149 13th Street, Charlestown, MA 02129, USA b Department of Psychology, University of New Mexico, Logan Hall, Albuquerque, NM 87131, USA c Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Fruit Street, Boston MA 02120, USA Received 11 December 2005; revised 7 July 2006; accepted 20 July 2006 Available online 25 September 2006 The prefrontal and temporal networks subserving object working memory tasks in adults have been reported as immature in young children; yet children are adequately capable of performing such tasks. We investigated the basis of this apparent contradiction using a complex object working memory task, a Categorical n-back (CN-BT). We examined whether the neural networks engaged by the CN-BT in children consist of the same brain regions as those in adults, but with a different magnitude of activation, or whether the networks are qualitatively different. Event-related fMRI was used to study differences in brain activation between healthy children ages 6 and 10 years, and young adults (20–28 years). Performance accuracy and RTs in 10-year-olds and adults were comparable, but the performance in 6-year-olds was lower. In adults, the CN-BT was highly effective in engaging the bilateral (L > R) ventral prefrontal cortex, the bilateral fusiform gyrus, posterior cingulate and precuneus, thus suggesting an involvement of the ventral visual stream, with related feature extraction and semantic labeling strategies. In children, the brain networks were distinctly different. They involved the premotor and parietal cortex, anterior insula, caudate/putamen, and the cerebellum, thus suggesting a predominant involvement of the visual dorsal and sensory-motor pathways, with related visual–spatial and action cognitive strategies. The findings indicate engagement of developmental networks in children reflecting task-effective brain activation. The age-related pattern of fMRI activation suggests a working hypothesis of a developmental shift from reliance on the dorsal visual stream and premotor/striatal/cerebellar networks in young children to reliance on the ventral prefrontal and inferior temporal networks in adults. © 2006 Elsevier Inc. All rights reserved. Keywords: Development of neural networks; Visual working memory with categorization; Ventral and dorsal visual streams; Cortical/striatal/cerebellar network; Event-related fMRI

⁎ Corresponding author. MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, 149 13th Street, Charlestown, MA 02129, USA. Fax: +1 617 726 7422. E-mail address: [email protected] (K.T. Ciesielski). Available online on ScienceDirect (www.sciencedirect.com). 1053-8119/$ - see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2006.07.028

Introduction The historical view of a child as a homunculus lingers in current neuroimaging research and management of pediatric patients, as we tend to focus on those brain areas in children that are essential for adult complex cognitive processing, such as the prefrontal cortex (Welsh and Pennington, 1988). The functional significance of the prefrontal cortex is hardly comparable between adults and young children, since it remains immature at least till adolescence (Adleman et al., 2002; Case, 1992; Casey et al., 1995, 1997; Ciesielski et al., 2004a; Diamond, 2002; Giedd et al., 1999; Klingberg et al., 1999; Luciana and Nelson, 1998; Sowell et al., 1999). Moreover, a similar frontal brain lesion in a child and adult may produce vastly different neurobehavioral symptoms and, conversely, similar symptoms can reflect damage in different brain networks (Goldstein, 1925; Luria, 1963; Vygotsky, 1934). Despite the prefrontal cortex immaturity, children can perform complex visual working memory tasks with an adult-like degree of accuracy (Bunge et al., 2002; Ciesielski et al., 2004b; Luna et al., 2001). The present study explores the bases for these apparent contradictions using event-related fMRI in a model of a complex visual working memory task involving categorical judgment. Working memory, in everyday cognitive tasks, refers to time limited processes of active representations of information, which is accessible for recall or for further manipulation (Baddeley, 1986). Working memory is an outcome of sustained attentional focus on task-relevant mental representations and on suppression of competing distracting events (Engle et al., 1999). Effective use of mental representations, actively held “on line”, has been found to be critical for behavioral and cognitive flexibility (Gevins and Smith, 2000; Goldman-Rakic, 1987) and a sensitive marker of cognitive development (Johnson et al., 2001; Luria, 1973). The neural mechanisms of working memory in adults rely on selective inhibitory processing related to the prefrontal brain networks (Goldman-Rakic, 1995; Luria, 1973). Categorical judgements (semantic, motor-perceptual or subliminal) about objects are

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essential parts of performance in object-related-working memory tasks. Consistent with the latter are neuroimaging findings showing a different location and amount of sustained activity dependent on the type of information held in working memory (Courtney et al., 1996; Desimone and Ungerleider, 1989; Gabrieli et al., 1998; Smith and Jonides, 2000). In adults, networks for object working memory tasks involving categorization of faces, objects and animals are located in the same brain regions: lateral inferior fusiform gyrus and inferior prefrontal gyrus (Chao et al., 1998). Similarly, networks engaged in processing of spatial working memory tasks are physically close to networks engaged in categorization by spatial location, action and movement (Martin et al., 2000a). Furthermore, clinical populations with lesions of brain areas subserving categoryspecific performance are also deficient in performance on objectworking memory tasks (Damasio et al., 1996). Thus in adults, the network for visual working memory has been identified with several distinct regions in the frontal and temporal cortex, including the inferior frontal, anterior insula area, posterior frontal and the anterior frontal middle gyrus, as well as the superior frontal sulcus, middle temporal and occipitalparietal areas (Haxby et al., 2000; Jonides et al., 1993; Owen et al., 1996; Petit et al., 1998; Smith et al., 1996; Wagner et al., 2001). Specifically, the areas of the inferior temporal/fusiform gyrus and middle occipital cortex are specifically engaged in working memory tasks involving objects, animals and faces (Gerlach et al., 2000; Kanwisher et al., 1997; Konorski, 1967; Smith and Jonides, 2000; Ungerleider and Mishkin, 1982). The inferior parietal/occipital region, superior frontal sulcus and middle temporal region are mostly involved in working memory tasks requiring spatial and movement judgement (Courtney et al., 1996; Pandya et al., 1988). In children, the findings concerning visual working memoryrelated networks are more variable. Several neuroimaging studies showed locations of activation similar to adults but with a more diffuse, widespread, lower power activation (Casey et al., 2002; Nelson et al., 2000; Thomas et al., 1999). Other studies show somewhat different patterns of activation in adults and children, and increased cortical activation with age (Gaillard et al., 2003; Klingberg et al., 2002; Kwon et al., 2002; Rubia et al., 2000; Thomas et al., 1999). The proposed regionally weighted models of development (Berl et al., 2005; Casey et al., 2002; Ciesielski et al., 1999; Rubia et al., 2000) have indicated a developmental trend to less subcortical and greater neo-cortical activation. In contrast to adults, children are reported to engage the early-maturing, posterior brain regions during effective response inhibition (Bunge et al., 2002; Ciesielski et al., 2004a). Some of these differences between children and adults may be influenced by nonstandardized task complexity, a wide range of age in tested children and differences in the conceptual approach to brain development (Berl et al., 2005; Gaillard et al., 2001). To examine the influence of age on working memory networks, we included two age groups of healthy children (6- and 10-yearolds) and young adults. Testing 6- and 10-year-old children allowed us to capture the largest group-age contrasts in development of functions in frontal networks (Luciana and Nelson, 1998; Passler and Isaac, 1985). Moreover, we chose 6-year-olds because at six the head circumference stabilizes, facilitating a reliable acquisition of fMRI signals across age groups (Caviness et al., 1996; Giedd et al., 1996), and at approximately 6 years of age children begin to be proficient in discriminating animals vs. non-

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animals (Livesey and Morgan, 1991; Thibault, 1999). As far as we are aware, our 6-year-olds constitute the youngest pediatric population in the fMRI literature. A new complex variant of the traditional n-back task (Gevins and Cutillo, 1993) has been designed as a “fun” computer game for children (Categorical N-Back Task: CN-BT; Ciesielski et al., 2004a,b). The CN-BT involves a rapid, random presentation of pictures, a fast button press to a stimulus–target after a categorical judgment whether the two objects preceding the stimulus–target belong to the same category of animals. It is a complex object working memory paradigm comprising stringent attentional focusing, active memory encoding, cognitive categorical judgment and fast motor response. It maximizes demands for executive reasoning, while holding memory demands constant. Consistent with the above review of findings on object-related working memory networks (Haxby et al., 2000; Pandya et al., 1988; Petersen et al., 1988; Wagner et al., 2001), we predicted that adults, while performing the CN-BT, will employ semantic strategies and will engage networks involving the ventral prefrontal, along with the inferior temporal regions, while performing the CN-BT. In children, the prefrontal and temporal regions mature late, therefore these regions may be involved to a different degree of magnitude, and/or may be complemented by engagement of other earlier maturing networks. Materials and methods Participants Twenty-seven healthy right handed volunteers completed the study and were paid for their participation: ten young adults [5 males; 20.4–27.6; mean 23.5 (2.29)]; nine 6-year-old children [5 males, range 5.11–6.6; mean 6.10 (SD = 0.55)] and eight 10-yearold children [4 males; range 9.10–10.5; mean 10.1 (0.45)]. Excluded from data analysis were: three children’s data sets due to excessive movement confound and technical problems during data acquisition, and two adults, due to disclosure of medication intake. Informed written consent was obtained according to the declaration of Helsinki. The study was approved by the MGH Institutional Review Board. Behavioral testing and preparation for imaging A neuropsychological battery of tests assessing attention, memory and executive inhibitory control was used about 7–10 days before scanning. A clinical interview included milestones of development, adaptive, social and academic functioning, and family screening for neurological and psychiatric disorders. The current report refers to performance on the Stroop Word–Color Interference Test, Wisconsin Card-Sorting Test and FAS Verbal Fluency Test. Subjects whose performance was within the normative range for age (not exceeding − 1.3 SDs), and who had a non-significant family clinical background were invited for the neuroimaging part of the study. Immediately after completion of the behavioral testing, participants were introduced to the MRI scanning room, equipment and the research staff whom she/he would meet during the future fMRI session. We did not use a mock scanner, as we found mock scanner experience interfering with the child’s expectations during the fMRI scanning session. Before the actual fMRI scanning, each child participated in a relaxation session, helping to control anxiety and motor movements in the scanner.

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Categorical N-Back Task The new Categorical N-Back Task (Ciesielski et al., 2004a,b) involved 72 commercially available color drawings of people, non-animate objects (buildings, cars, fruits, plants) and animals (mammals, birds, reptiles, fish, insects) which were presented consecutively, in random order (Fig. 1). Small crosses were randomly intermixed with picture stimuli to provide a fixation point and irregularity of timing. A drawing of a raccoon served as the n-back target. When a raccoon was presented, the subject was required to press a button with his/her right forefinger, only if at least two drawings prior to the raccoon belonged to the category of animals; or to press a button with the left forefinger to any other combinations of stimuli preceding the raccoon. This task posed a strong demand on selective attending, active mental reasoning and flexibility of operations, and on making a fast categorical judgment followed with a motor response. The memory demands remained constant across the whole task. To suit the processing capacity of the younger children, a behavioral pilot study was conducted with additional group of six 6-year-old youngsters to verify that they are able to perform the task. The accuracy rate was between 70 and 86%. However, children had much longer reaction time for correct responses (RT = 820 ms) than adults (RT = 476 ms). The following task parameters were chosen: (1) a stimulus duration of 500 ms; (2) a post-target stimulus time delay of 1500 ms; to provide time for younger children to press the button; (3) a visual angle of stimuli ~ 3.4° vertically and ~ 2.6° horizontally. Stimuli were presented with the MacStim DIO −24 (Cogstate LTD. Australia, D. Darby). Stimuli were generated on a Macintosh PowerBook G4 and projected, via a Sharp XG-2000V color LCD projector through a collimating lens onto a rear-projection screen that was secured vertically in the magnet bore at neck level. Subjects viewed the images on a tilted mirror placed directly in front of their eyes. Each run consisted of 124 stimuli (25% raccoons, ~ 23% of other animals, 22% non-animals, 30% crosses), and lasted 2 min 32 s. The current report presents data from the first two runs. Over the duration of the study (~30 min fMRI, ~ 20 min

structural imaging), subjects attended 10 runs of stimuli related to three different tasks. fMRI data acquisition Whole-head fMRI data were acquired using a 1.5 T Siemens Sonata scanner (Erlangen, Germany). Changes in blood oxygen level-dependent T2*-weighted MRI signals were measured using a gradient-echo EPI sequence (TR = 2000 ms, TE = 30 ms, FOV = 200 mm, 64 × 64 matrix, in-plane resolution 3.125 mm × 3.125 mm; 22 slices 6 mm thick, inter-slice gap 1.2). A matching single-volume spin echo (EPI) was obtained for registration purposes. High-resolution 3D structural images were acquired (TR = 2.7 s, TE = 3.4 ms, FOV = 256 mm, 256 × 256 matrix, slab thickness 170 mm, voxel size 1.3 mm × 1.0 mm × 1.3 mm; inversion and excitation pulses were non-selective). The duration of each of two MP-RAGE scans was 8 min 46 s. These T1-weighted images provided a detailed structural base for registration and 3D normalization to the Talairach and Tournoux (1988) atlas. During scanning of children, we always had an adult person with the child in the scanning room. Data analysis Behavioral analyses were performed using SPSS 9.0 for Windows (http://www.spss.com). Group differences in mean percent correct responses and mean reaction times to correct responses in the Categorical N-Back Task were examined with two-tailed t tests for independent groups, at a significance level of p = 0.05. Additionally, results from several neuropsychological tests were also examined, with two-tailed t tests for independent groups, at a significance level of p = 0.05. Scores were age-scaled where appropriate. The neuropsychological variables included results from the Stroop Word–Color Interference Test, Wisconsin Card-Sorting Test and FAS Verbal Fluency Test. Because of the small number of subjects in each group, Levene’s test for equality of variances was first performed on each analysis, to test the assumption of homogeneity of variance between groups. In cases

Fig. 1. Categorical N-Back Task (CN-BT). This novel variant of the n-back task requires fast and accurate recognition of drawings of animate and non-animate objects presented sequentially, and a button-press to a target stimulus raccoon if the two prior events belong to a category of animals. It is a challenging task for flexibility and speed of mental processing, and selective inhibitory control.

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where Levene’s test was significant, t tests using unequal variances were used. Two-tailed zero-order Pearson correlations were performed between the neuropsychological variables, and the performance on the Categorical N-Back Task. To examine the areas of activation related to the CN-BT, the within-group random-effects analyses (Holmes et al., 1998) were performed using contrast images and one-sample t tests. Because expected areas of activation were strongly hypothesis-driven and to maximize sensitivity to category effects in relevant regions of the cortex in all group of subjects (Mechelli et al., 2003), the results are reported at p < 0.001 (uncorrected for multiple comparisons) with a spatial extent threshold of at least 5 contiguous voxels. fMRI data were preprocessed and analyzed using SPM99 (Welcome Department of Cognitive Neurology, University College London Medical School, London, UK; http://www.fil.ion.ucl.ac. uk/spm). For preprocessing, functional images were realigned using sinc interpolation, corrected for differences in slice acquisition time by temporal realignment to the middle slice using sinc interpolation. They were normalized to the SPM99 default EPI template using bilinear interpolation to 2 × 2 × 2 mm voxels, and spatially smoothed with a Gaussian filter of 6 mm full width-half maximum. Results from previous studies indicate that normalization of children’s MR images to standard adult templates are useful for statistical group comparisons, and that this is acceptable considering the age of our participants and image voxel size used (Burgund et al., 2002; Kang et al., 2003). Temporal data were filtered with a high pass filter using the default session cutoff period of “two times the longest interval between two appearances of the most frequently occurring event” to correct for lowfrequency noise, and a low pass Gaussian filter of 4 s to correct for temporal autocorrelations. Analyses were performed within the framework of the General Linear Model and theory of Gaussian random fields (Friston, 1994). fMRI responses were modeled by a canonical hemodynamic response function and its temporal derivative with realignment (movement) parameters included as covariates (Friston et al., 1998). Data for each subject were first analyzed with a fixed effects model to produce contrast images, which were then employed for group analyses using a random-effects model (Holmes et al., 1998). The responses to raccoon stimuli preceded by at least two consecutive animal stimuli were modeled in SPM as correct n-back responses. At the single subject level, the contrast of comparison was correct n-back response vs. baseline (n-back > baseline), where the baseline comprised all trials that did not require a response to the n-back target. The designations of significantly activated areas were determined using the Talairach Daemon Client software program, version 1.1 (Research Imaging Center, University of Texas Health Science Center at San Antonio). Estimated motion parameters were computed by SPM. A one-way ANOVA across groups indicated

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that groups did not differ significantly in mean group motion: (I) for the x axis (mean group movements: 6-year-olds 0.020 mm, 10year-olds 0.034 mm, adults 0.018 mm) [F(23) = 0.722, p = 0.49]; (II) for the y axis (0.118 mm, 0.153 mm, 0.189 mm, respectively) [F(23) = 0.465, p = 0.634]; (III) for the z axis (0.200 mm, 0.136 mm, 0.055 mm, respectively) [F(23) = 1.67, p = 0.210]. Since the correct responses evoke a stronger activation and coupling in distributed cortical networks (Pessoa et al., 2002), and provide a clarity and validity across groups, only correct response trials were submitted for analysis. To identify regions of activation correlated with age, performance and speed of response, three separate simple linear regression analyses were performed across groups (using SPM99) using age (threshold p = 0.01), mean percent of correct responses (p = 0.001) and mean RTs for correct responses (p = 0.005) as covariates. For RTs, a negative correlation was conducted, to find regions of activation correlated with faster responses. Additionally, two partial correlation analyses were performed: (1) the covariates age and percent correct responses, with age partialled out (p = 0.001), (2) the covariates age and mean RTs, with age partialled out (p = 0.05). The regression analyses employed a statistical threshold of p < 0.01, 0.005 or 0.001, with an extent threshold of five contiguous voxels. Results Behavior Table 1A presents performance on the CN-BT task and neuropsychological tests (Stroop Word-Color Interference, Wisconsin Card-Sorting and FAS-Verbal Fluency). In the CN-BT task 10-year-old children and adults performed on a similar level of accuracy (87% vs. 95%). The 6-year-olds had significantly lower correct rates than adults, however, still above random level, with one child achieving 100% accuracy. The accuracy of 10-year-old children was not significantly different from the adults. All participants performed on or above the age norm in standard neuropsychological measures. Group differences show significantly lower scores in 6-year than 10-year-olds on WCST (p = 0.001), FAS (p = 0.002) and the CN-BT (p = 0.001), slower responses in children than adults on the Stroop task (Word/Color: for 10 vs. adults, p = 0.008; for 6 vs. adults, p = 0.004), and statistically significantly lower scores in 6-year-old children than adults in all measures. These standard neuropsychological tests, commonly associated in the literature with executive inhibitory control (Lezak, 1995), are significantly correlated with the CN-BT. Table 1B presents the results of the Pearson r correlation between accuracy and RTs on the CN-BT task and performance on FAS-Word Fluency, Stroop Word–Color and WCST. The high correlation between the CN-BT and the neuropsychological measures indicate some functional

Table 1A Performance rate on neuropsychological tests and on CN-BT task: means and standard deviations (in parentheses) for Stroop Interference Test: W/W = word/ word, C/C = color/color, W/C = word/color; for WCST: CAT = categories, error persev. = perseverative errors; and for FAS = Word Fluency Test Stroop W/W Stroop C/C Stroop W/C WCST number WCST number FAS number number corrects number corrects number corrects CAT achieved error persev. of words 6 years, n = 9 47.29 (20.16) 10 years, n = 8 83.25 (14.18) Adults, n = 10 104.40 (42.69)

48.00 (27.72) 57.25 (9.57) 92.70 (50.63)

23.43 (9.16) 28.50 (8.52) 52.80 (21.22)

4.56 (1.94) 7.25 (2.50) 8.10 (3.45)

32.78 (11.39) 21.38 (11.49) 9.20 (7.59)

Categorical n-back % corrects

Categorical n-back: RTs (ms) to corrects

16.11 (4.65) 66.23 (15.65) 760 (150) 26.25 (6.14) 87.00 (10.62) 490 (120) 40.10 (16.57) 95.22 (3.82) 480 (150)

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Table 1B Correlation between % corrects for the CN-BT and neuropsychological measures: Stroop Word–Color Test, Wisconsin Card Sorting Test and FAS Word Fluency Test

% Cor CN-BT

Stroop

Stroop

Stroop

WCST

WCST

W/W

C/C

W/C

No. CAT

% ERR

FAS

0.337 0.085

− 0.559 0.002

0.588 a 0.001

0.583 0.002

a

0.399 0.048

b

0.547 0.005

a

FAS

a

% Cor = percent of correct responses. a Correlation is significant at the 0.01. b Correlation is significant at the 0.05.

familiarity between these tests, in which a considerable component represents a selective inhibitory control. Brain imaging Within-group analysis Examination of brain activation during performance on the CNBT task within each subject group shows a pattern which was determined by age (Fig. 2).

Adults. A significant activation was found in the left prefrontal ventral/orbital (BA 47, 45 and 11) and medial prefrontal (BA 10) regions, the ventral temporal cortex including the fusiform gyrus (BAs 20, 37), the putamen and posterior (BA 31) cingulate cortex. The activation in the motor (BA 4) and superior frontal gyrus (BA 8) was recorded with a lower number of voxels. There was some activation in the cerebellum, vermal lobuli VIIIa and VIIIb (Makris et al., 2003; Schmahmann et al., 2000). The pattern of activation appears consistent with the involvement of areas within the ventral visual stream (Table 2). 10-year-old children. The number of activated voxels is high in the premotor cortex (BA 6), putamen/insula (BA 13) and in the superior/inferior parietal region (BA 40/7). A lower number of activated voxels is found in the middle prefrontal cortex (BA 9), the anterior cingulate gyrus (BA 24), the inferior prefrontal (BA 44/45) cortex and the inferior/ventral temporal region (BA 37). We observed significant activation in the cerebellar vermal lobuli VII and VIII, and the posterior cerebellar hemisphere (Crus II; Makris et al., 2003) (Table 3).

Fig. 2. Pattern of brain activation to Categorical N-Back Task in adults and children. Adults show more activation in the inferior/ventral prefrontal and inferior temporal regions; children show more activation in the premotor cortex, basal ganglia and cerebellum. The results suggest a developmental shift in the brain networks from dorsal visual stream and cerebellum in children to ventral visual stream and prefrontal cortices in adults.

K.T. Ciesielski et al. / NeuroImage 33 (2006) 980–990 Table 2 Young adults: regions of activation to the correct responses during performance on the Categorical N-Back Task Region

BA a

Volume b

Z score

Coordinates c

Frontal L inferior L inferior L medial R medial R inferior/middle R precentral R superior

47/11 45 10 9/10 46 4 8

326 50 223 277 39 86 35

4.67 4.28 4.33 4.19 3.81 4.20 3.78

− 26 8 −16 − 54 36 4 − 4 56 −4 16 54 2 36 30 12 30 −18 38 22 14 40

31 24 31/7

24 23 158

3.67 3.73 4.39

− 8 − 54 20 22 44 8 8 −44 36

Limbic lobe L posterior cingulate R anterior cingulate R posterior cingulate/precuneus Temporal lobe L inferior/fusiform R inferior R anterior insula/postcentral Sub-lobar R putamen L cerebellum

20/37 20 13/43

nBA nBA

100 17 168

116 113

4.12 3.86 3.92

4.69 4.10

− 58 − 4 − 28 48 −12 − 36 54 −14 20

28 −6 36 − 24 − 42 −32

nBA = no Brodmann's Area. a BA = Brodmann's Area. b Volume in mm3. c x, y, z are Talairach coordinates for maximum Z score of cluster.

6-year-old children. Significant activation was found in the bilateral premotor cortex (BA 6), the anterior cingulate (BA 24), the superior parietal (BA 7) cortex, the insula/caudate region (BA 13), superior temporal region (BA 22), posterior cerebellar vermal lobuli VII and posterior lateral hemisphere (R > L; Crus I; Schmahmann et al., 2000). The pattern of activation was more consistent in the brain components within the dorsal visual stream than the ventral visual stream. Relatively small voxel volume activation was observed in the inferior frontal cortex (BA 44/45) (Table 4). Between-group analysis To examine the developmental differences in brain activation, a between-group random-effects analysis was performed using contrast images and t tests for independent samples. Activation between adults and 6-year-olds was examined at a threshold of p = 0.005 (uncorrected); between adults and 10-year-olds at p = 0.05; and between 10-year-olds and 6-year-olds at p = 0.005 (uncorrected). Regions activated more in older than younger subjects. In adults vs. 6-year-old children, the regions included: the L inferior 3 frontal regions (BA 47/45 [−24 8 − 14], z = 3.98, 271 mm ), L 3 posterior cingulate (BA 31; [− 6 0 64], z = 3.92, 43 mm ); and R prefrontal ventral/orbital (BA 47/11 [36 28 12], z = 3.16, 3 141 mm ). In adults vs. 10-year-old children, the regions of greater activation included: the L prefrontal ventral/orbital region 3 (BA 47/11 [−34 −24 12], z = 2.48, 56 mm ), inferior temporal 3 region (BA 41[60 −26 24], z = 2.85, 116 mm ); R inferior

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Table 3 Ten-year-old children: regions of activation to the correct responses during performance on the Categorical N-Back Task Region

BA a

Volume b

Z score

Coordinates c

Frontal R inferior R middle R premotor

45/44 9/8 6/4

31 129 328

3.98 4.30 4.13

48 30 4 36 16 26 44 − 6 46

Limbic lobe L anterior cingulate R anterior cingulate

32/24 24

64 131

4.07 4.24

−20 20 32 2 30 12

Temporal lobe L fusiform L extrastriate cortex

20/37 19

64 17

4.43 3.78

−56 − 14 − 22 −54 − 70 − 2

Parietal lobe R superior/inferior

40/7

69

3.82

−62 − 32 30

Occipital lobe L middle/inferior R middle

19/18 19

184 33

4.08 3.61

−30 − 76 16 28 − 96 20

Sub-lobar Insula/Putamen R cerebellum

13/nBA d nBA

613 89

3.95 3.93

36 − 18 0 48 − 56 − 34

a b c d

BA = Brodmann's Area. Volume in mm3. x, y, z are Talairach coordinates for maximum Z score of cluster. nBA = no Brodmann's Area.

Table 4 Six-year-old children: regions of activations to the correct responses during performance on the Categorical N-Back Task Region

BA a

Volume b

Z score

Coordinates c

Frontal L Premotor R Inferior R Middle R Premotor

6 44 8 6/4

149 16 12 239

4.71 3.53 3.61 4.25

−6 0 64 50 10 18 50 22 46 12 − 28 40

Limbic lobe L anterior cingulate R anterior cingulate

24/23/32 24

28 8

3.59 4.02

−4 − 20 30 4 − 10 42

Temporal lobe L fusiform R superior/midtemporal

37 22

56 58

4.14 4.29

−48 − 44 − 10 52 0 6

Parietal lobe L inferior R superior/inferior

40 7/40

6 89

3.35 3.85

−64 − 40 28 62 − 18 22

Sub-lobar area L caudate/L insula R caudate

nBA d/13 nBA

210 69

4.51 3.70

−38 − 20 22 18 2 26

L cerebellum R cerebellum

nBA nBA

66 47

4.02 4.08

−16 − 44 − 32 18 − 36 − 50

a b c d

BA = Brodmann's Area. Volume in mm3. x, y, z are Talairach coordinates for maximum Z score of cluster. nBA = no Brodmann's Area.

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prefrontal area (BA 45/46 [40 28 20], z = 2.47, 176 mm ); 3 inferior temporal area (BA 37 [46 − 42 −14], z = 2.46, 79 mm ), and posterior cingulate (BA 31). Major regions of greater activation in 10-year-olds vs. 6-year-olds included: L fusiform gyrus/extrastriate cortex (BA 37/19 [−24 60 −6], z = 4.35, 3 393 mm ), L prefrontal inferior/ventral area (BA 47 [− 24 8 3 −12], z = 3.61, 188 mm ); L anterior cerebellum ([− 24 −48 3 −22], z = 3.25, 168 mm ); and R prefrontal inferior ventral 3 region (BA 47 [26 14 −12], z = 3.74, 67 mm ). Regions activated more in younger than older subjects. Regions of larger volume of activation in 6 years old children than in adults included: L premotor region (BA 6 [−28 − 12 62], z = 3.70, 393 mm3); L anterior cingulate (BA 24/23 [−2 − 20 30], z = 4.14, 474 mm3); L caudate/insula ([−18 −34 16], z = 3.26, 32 mm3); R premotor cortex (BA 6 [12 −24 46], z = 4.27, 212 mm3); R right cerebellar hemisphere ([12 −36 −50], z = 4.87, 96 mm3). Regions of greater activation in 10-year-old children than in adults included: R cerebellum ([28 − 58 − 24], z = 4.19, 222 mm3); L premotor cortex (BA 6 [− 32 − 12 66], z = 3.68, 65 mm3); R premotor cortex (BA 6 [36 − 6 40], z = 3.57, 78 mm3); R extrastriate occipital cortex (BA 19 [− 46 −44 − 12], z = 3.85, 135 mm3). Regions that showed significantly greater activation in 6-yearold children as compared to 10-year-old children included: L superior temporal/inferior parietal region (BA 22/40 [56 − 42 22], z = 3.19, 39 mm3); R inferior parietal region (BA 40 [60 − 24 16], z = 3.24, 52 mm3); R cerebellum ([42 − 72 − 22], z = 2.90, 29 mm3). Regression analysis The results of simple correlations are shown in Table 5 and Fig. 3A. The increasing age of subjects correlates positively (p < 0.01) with the R and L prefrontal ventral/orbital region and the middle frontal area (BA 10). Higher accuracy of responding is associated with greater activation (p < 0.001; Table 5B) mostly in the L prefrontal ventral/orbital cortex (BA 11/47), R parahippocampal area and bilateral inferior temporal area (BA 21/20/37). The

Table 5 Regions of activation positively correlated with: (A) age, (B) percent corrects, (C) negative correlation with RTs Region

BA a

A. Age L inferior frontal L middle frontal R inferior frontal R superior parietal

10 47 7

B. Percent correct L inferior frontal/orbital L parahippocampal gyrus R middle frontal R inferior temporal

47/11 30 10 21

Volume b

Z score

Coordinates c

96 28 123 41

3.12 3.06 3.37 3.13

− 24 8 − 14 − 38 48 − 6 38 28 14 48 −18 60

1074 149 357 40

5.50 4.52 4.40 3.97

− 40 46 − 8 − 10 − 42 −10 44 42 10 62 −10 − 18

C. Regions negatively correlated with RTs L inferior frontal 47 308 L fusiform/extrastriate 37/19 57 R inferior frontal 47 53 R inferior temporal 21/22 17 a b c

5.12 4.44 3.65 3.62

− 24 14 − 12 − 24 − 60 −6 24 12 − 14 60 −12 − 16

BA = Brodmann's Area. Volume in mm3. x, y, z are Talairach coordinates for maximum Z score of cluster.

Fig. 3. (A) Simple correlation between fMRI activation areas and % of correct responses in the Categorical N-Back Task. Please note the clear relationship of the CN-BT to the prefrontal–ventral, particularly over the left brain hemisphere. (B) Partial correlation with age partialled out. The results of regression analysis suggest that the Categorical N-Back Task is an effective cognitive tool for activation of the inferior/ventral/orbital prefrontal regions (L > R).

negative correlations (Table 5C) showed that activation increases in the bilateral prefrontal ventral/orbital cortex (BA 47, 11 L > R), left temporal ventral region (Fusiform Gyrus, BA 37/19), left inferior temporal gyrus (BA 21), left anterior cerebellum and parahippocampal gyrus with shorter RTs (p < 0.001). The results of partial correlation analyses (Table 6, Fig. 3B) for percent corrects in the scanner and age, with age partialled out, showed a significant activation (p < 0.001) in: L prefrontal inferior/ ventral region (BA 47/11), bilateral middle frontal region (BA 10/ 46) and bilateral parahippocampal gyrus. Discussion Distinctly different brain networks were engaged in children and in adults during processing of the same visual working memory task, the Categorical n-back (CN-BT). In adults, the pattern of activation involved the bilateral (L > R) inferior/ventral

Table 6 Partial correlation analyses: covariation of age and percent correct responses with regions of activation, with age partialled out (p = 0.001) Region

BA a

Volume b

A. Age and % corrects, with age partialled out L inferior frontal/orbital 47/11 840 L medial frontal 9 13 L middle frontal 10/46 146 L parahippocampal 58 R middle frontal 10/9 233 a b c

Z score

Coordinates c

5.00 3.56 3.71 4.06 4.51

− 38 14 − 10 − 18 30 28 − 32 48 12 − 28 − 16 − 12 44 40 10

BA = Brodmann's Area. Volume in mm3. x, y, z are Talairach coordinates for maximum Z score of cluster.

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and medial prefrontal cortex, the bilateral inferior temporal cortex (fusiform gyrus), posterior cingulate, precuneus and the cerebellum. This pattern suggested involvement of the object-processing ventral visual pathway (Gerlach et al., 2000; Kastner and Ungerleider, 2000; Martin et al., 2000a; Smith and Jonides, 2000). Thus, the CN-BT was effective in engaging the ventral prefrontal network in adults, as reflected in the high correlations between the ventral prefrontal activation and response accuracy, shorter RTs and older age. However, the task was not effective in engaging the prefrontal networks in children. In children the pattern of activation included: premotor, superior/inferior parietal and middle temporal cortex, anterior insula, caudate/putamen, and the cerebellum. These areas are closely related to sensory-motor and dorsal visual pathways, and are associated with visual–spatial action-related and complex cognitive processing (Desmond, 2001; Grafton et al., 1997). Before attempting to interpret these child–adult differences in brain networks, it is important to determine that the differences do not result just from a higher task difficulty and higher attentional demands in children. First, one would expect that if the task difficulty has influence on the networks involved, then children performing worse than adults should have different networks, and children on the same level of accuracy and speed of performance should have the same networks as adults. However 10-year-olds performed with a level of accuracy similar to adults (87% vs. 95%), and yet the involved networks were different. Furthermore, one of the 6-year-old boys achieved an unusually high rate of 100% corrects, but his pattern of fMRI activation was closer to other children, than to the adults. Second, since only the event-related fMRI signals to correct responses were analyzed (see Pessoa et al., 2002), the activation to errors did not confound the results. Despite the 6-year-olds poorer performance in the scanner, their pattern of brain activation was similar to 10-year-olds, who performed as well as the adults. Third, if higher task difficulty would have influenced the networks in children, then one would expect to see more frontal activity, particularly in 6-year-old children, as an increased prefrontal activation has been reported with increased levels of task difficulty (Braver et al., 1997; Duncan and Owen, 2000 review; D’ Esposito et al., 1998; Gaillard et al., 2001; Just et al., 1996). However, we found less prefrontal activation in children. Fourth, it was not the case that in children there was an overall less activation, as we observed more activation in premotor and striatal regions in children as compared to adults. In conclusion, it is unlikely that the current findings, suggesting different networks in children and adults, resulted from factors which are non-specific to development, such as a general task difficulty. Our findings are consistent with a gradual developmental shift from the early maturing premotor/striatal/parietal/cerebellar networks in children to ventral prefrontal/inferior temporal networks in adults. The development of semantic processing is an essential component of this maturational process (Poldrack et al., 1999; Gabrieli et al., 1998). Current findings are clearly supportive of Leo Vygotsky’s (1934) model of brain development. Based on clinical and educational studies Vygotsky proposed that the identical task performed by adults and children may involve radically different organizational cognitive processes, performance strategies and, therefore, dynamic functional brain subsystems. The functional brain subsystems are gradually altered through external and internal experience from one stage of development to the next (Van Der Veer and Valsiner, 1993; Vygotsky, 1934). Our view has also some commonalities with the concept of a “discontinuous

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transition” of networks in development (Rubia et al., 2000), which suggests that the mature task-related network, involving the frontal lobes, takes over as soon as task-related performance surpasses the capacity of the immature network. However, our data invite an alternative interpretation for some fMRI studies, which suggested that children activate broader, task-irrelevant brain areas, as compared to task-relevant activation in adults (Booth et al., 2001; Casey et al., 2002); an engagement of the seemingly taskirrelevant areas in children may, in fact, reflect an engagement of the most effective task-relevant networks attainable at a particular stage of brain maturation. Since studies in adults have shown that alternative strategies in solving the same task may engage different neural networks (Smith et al., 1998), we suggest, by analogy, that different task strategies used by children might have contributed to the different networks among children and adults. For example, the same tasks, when solved by using action and spatial relationship strategies, have been reported to evoke, in adults, foci within visual dorsal stream structures, the premotor cortex, the parietal cortex and the cerebellum (Braddick et al., 2001; Desimone and Ungerleider, 1986; Gerlach et al., 2000; Zeki et al., 1991), whereas when solved by using semantics they engaged the ventral stream (Martin et al., 2000b; Grafton et al., 1997; Shmuelof and Zohary, 2005). In our study, children activated more dynamically the dorsal visual stream, thus one may speculate that they more readily used strategies based on animation and action. Visual recognition, memorization and categorical judgment (components of complex working memory tasks such as CN-BT) show age-related changes (Gutheil and Gelman, 1997; Smiley, 1987), with children 5 to 6 years of age being more inclined to perceive objects based on animation than on static or semantic object properties (Booth and Waxman, 2002; Mervis and Crisafi, 1982; Thibault, 1999). Object animation is a powerful organizing principle in working memory tasks in children (Gelman and Coley, 1990), and although the animation rule is mostly evident in infants (Booth and Waxman, 2002), our young participants may still use it when faced with a challenging, effortful and time constrained task, such as CN-BT. In agreement with the above considerations, i.e., children more readily using animation strategies and dorsal networks, the theories of cognitive development, such as the one by Piaget (1971), suggest that sensorimotor learning is critical in early child development, as it forms a fundamental base for the later formation of abstract and logical operations. A model resembling the Piagetian theory was proposed more recently by Martin (1998). Martin suggests that codes for semantic representation of attributes, features and meaning of objects, are stored in the sensory and motor systems that are active during initial learning about these objects. Indeed, young children learn through action and intense sensory exploration and may be much more sensitive to the animate–inanimate distinction and, therefore, use more often the dorsal pathway when perceiving objects (Fergus et al., 2002; Leslie, 1984). This does not eliminate a degree of interactive use of both dorsal and ventral neural pathways, with the dorsal pathway actively developing at an earlier stage (Johnson et al., 2001). It is worth noting that dorsalstream vulnerability has been postulated in the biological bases of several neurodevelopmental disorders demonstrating primary working memory deficits (Atkinson et al., 2005; Braddick et al., 2003; Spencer et al., 2000). The involvement of the dorsal pathway in animation/motor strategies used by children in our study is consistent with evidence about the role of this cortical pathway in processing stimuli for the

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purpose of manipulating them in space (Sakata et al., 1997; Oztop and Arbib, 2002). Rizzolatti and Matelli (2003) describe two different streams within the networks traditionally associated with the dorsal visual pathway: one for controlling actions “on line” and the other for organization of action. They suggest that the learning of action and complex motor knowledge, precedes learning of perception and conceptual thinking (Rizzolatti and Matelli, 2003) and, therefore, that the movement/animation function may reside at the foundation of cognition. Evidence of existing connections between brain regions forming the dorsal and sensory-motor networks provides support to their functional relatedness. This includes the connections between the striatum and the cerebellar– cortical network (Clower et al., 2005; Hoshi et al., 2005), the parietal and the premotor regions (Chavis and Pandya, 1976; Petrides and Pandya, 1984; Luppino et al., 1999), and the cerebellum and the parietal cortex (Schmahmann and Pandya, 1997). These functional connections are consistent with the significance of the sensory-motor subcortical and posterior brain networks for the early cognitive functioning (Graybiel, 1995; Goldman, 1974; Greenough et al., 1993). Conclusions Our findings suggest that accurate performance on the working memory task involving categorization, the CN-BT, engages in children networks, which are qualitatively different from those in adults. This is in agreement with a concept proposed by Vygotsky (1934) about differential developmental brain subsystems. The network engaged in 6- and 10-year-olds has many properties of the dorsal visual stream and sensory-motor pathways and involved mostly the premotor/striatal/parietal/cerebellar network. The current study suggests an increase in proficiency and speed of performance on object working memory tasks with age, as well as increasing engagement of the inferior/prefrontal network. The pattern of fMRI activation suggests a working hypothesis on a developmental shift from reliance on a predominantly dorsal visual stream and sensory/motor networks, and therefore mostly on animation strategies in children, to reliance on the inferior/ventral prefrontal and temporal cortex, and therefore mostly on semantic strategies in adults. The current findings may offer an alternative interpretation of activation patterns in pediatric neuroimaging studies, and encourage new hypotheses related to the etiology of neurodevelopmental disorders.

Acknowledgments This work was supported in part by The National Center for research Resources (P41RR14075) and The Mental Illness and Neuroscience Discovery (MIND) Institute. We thank Drs. Bruce Rosen, Nikos Makris, Adele Diamond and Lynette Cofer for their support and help at different stages of this study, and Mss. Jean Mack and Sylvia Baedorf for help in testing children. References Adleman, N.E., Menon, V., Blasey, C.M., White, C.D., Warsofsky, I.S., Glover, G.H., Reiss, A.L., 2002. A developmental fMRI study of the Stroop color–word task. NeuroImage 16, 61–75. Atkinson, J., Braddick, O., Rose, F.E., Searcy, Y.M., Wattam-Bell, J., Bellugi, U., 2005. Dorsal-stream motion processing deficits persist

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