Altered executive function in the lead-exposed brain: A functional magnetic resonance imaging study

NeuroToxicology 50 (2015) 1–9

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NeuroToxicology

Altered executive function in the lead-exposed brain: A functional magnetic resonance imaging study Jeehye Seo a,1, Byung-Kook Lee b,1, Seong-Uk Jin a, Kyung Eun Jang a, Jang Woo Park a, Yang-Tae Kim c, Sin-Jae Park d, Kyoung Sook Jeong e, Jungsun Park f, Ahro Kim g, Yangho Kim h,*, Yongmin Chang a,i,j,** a

Department of Medical & Biological Engineering, Kyungpook National University, Daegu, South Korea Korean Industrial Health Association, Seoul, South Korea c Department of Psychiatry, School of Medicine, Keimyung University, Daegu, South Korea d Department of Psychiatry, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, South Korea e Department of Occupational and Environmental Medicine, Dongguk University College of Medicine, Dongguk University Ilsan Hospital, Goyang, South Korea f Department of Occupational Health, Catholic University of Daegu, Daegu, South Korea g Department of Neurology, Inje University Ilsan Paik Hospital, Goyang, South Korea h Department of Occupational and Environmental Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, South Korea i Department of Radiology, Kyungpook National University School of Medicine, Daegu, South Korea j Department of Molecular Medicine, Kyungpook National University School of Medicine, Daegu, South Korea b

A R T I C L E I N F O

A B S T R A C T

Article history: Received 20 May 2015 Received in revised form 8 July 2015 Accepted 8 July 2015 Available online 14 July 2015

Introduction: It is well known that lead exposure induces neurotoxic effects, which can result in dysfunction in a variety of cognitive capacities including executive function. However, few studies have used fMRI to examine the direct neural correlates of executive function in participants with past lead exposure. Therefore, this study aimed to investigate possible alterations in the neural correlates of executive function in the previously lead-exposed brain. Methods: Forty-three lead-exposed and 41 healthy participants were enrolled. During the fMRI scans, participants performed two modified versions of the Wisconsin Card Sorting Task (WCST) differing in cognitive demand, and a task that established a high-level baseline condition (HLB). Results: The neural activation of left dorsolateral prefrontal cortex was greater in healthy controls than in participants with lead exposure when contrasting the difficult version of the WCST with the HLB. Moreover, cortical activation was found to be inversely associated with blood lead concentration after controlling for covariates. Discussion: These data suggest that lead exposure can induce functional abnormalities in distributed cortical networks related to executive function, and that lead-induced neurotoxicity may be persistent rather than transient. ß 2015 Elsevier Inc. All rights reserved.

Keywords: Lead exposure Wisconsin Card Sorting Task Executive function fMRI

1. Introduction Occupational exposure to lead has declined steadily over the past 20 years (CDC, 2013), and thus present-day concerns center on

* Corresponding author at: Department of Occupational and Environmental Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, # 2903 Cheonha-Dong, Dong-Ku, Ulsan 682-060, South Korea. ** Corresponding author at: Department of Molecular Medicine, Kyungpook National University School of Medicine, 130 Dongdeok-ro, Jung-gu, Daegu 700-721, South Korea. E-mail addresses: [email protected] (Y. Kim), [email protected] (Y. Chang). 1 These authors contributed equally to this study. http://dx.doi.org/10.1016/j.neuro.2015.07.002 0161-813X/ß 2015 Elsevier Inc. All rights reserved.

the health effects of past exposure (Khalil et al., 2009; Schwartz et al., 2000). It is widely recognized that lead exposure has toxic effects on every organ system, especially the central nervous system. Lead-induced brain damage may result in a variety of neurological disorders, including mental retardation (Sanders et al., 2009), Alzheimer’s disease, Parkinson’s disease (MonnetTschudi et al., 2006), and schizophrenia (Opler et al., 2008). In addition, the neurotoxic effects of lead exposure may cause behavioral problems such as attention deficit hyperactivity disorder, juvenile delinquency, and criminality (Wright et al., 2008). In addition, lead can adversely affect general intellectual functioning (Sanders et al., 2009), visuospatial function (Weisskopf et al., 2007), and verbal learning and memory (Bleecker et al.,

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J. Seo et al. / NeuroToxicology 50 (2015) 1–9

2005). Furthermore, recent studies have demonstrated that executive function may be at particular risk from lead-induced neurotoxicity (Canfield et al., 2004; Trope et al., 2001). Executive dysfunction can appear immediately after lead exposure, or be delayed (Sanders et al., 2009). Most studies of executive dysfunction have been performed in children, and only a few studies in adults have examined workers exposed to lead (Canfield et al., 2004; Trope et al., 2001; Sanders et al., 2009). Adult occupational exposure and childhood developmental exposure to lead result in different cognitive and behavioral outcomes (Sanders et al., 2009; NTP, 2012). Thus, studies on workers exposed to lead are needed to clarify lead neurotoxicity related to executive functions. Executive function, one of the main functions of the prefrontal cortex, allows us to guide appropriate behavior contextually. The Wisconsin Card Sorting Task (WCST) has been used to examine executive dysfunction (Goldstein et al., 2004; Milner, 1963). During this task, the participants asked to match randomly drawn cards to reference cards in accordance with a rule of classification. Without notice, however, the rule is changed and the participant must switch from the previous rule to a new rule. Thus, the WCST involves a range of cognitive functions including working memory, set shifting, and error detection. There is mounting evidence of impairment on the WCST in patients with frontal cortex damage (Demakis, 2003; Goldstein et al., 2004; Mukhopadhyay et al., 2008; Stuss et al., 2000). Furthermore, most neuroimaging studies demonstrate that the WCST increases neural activation specific to executive function, especially in prefrontal cortex (GonzalezHernandez et al., 2002; Lie et al., 2006; Nyhus and Barcelo, 2009). Recently, neuroimaging research has investigated the prefrontal cortex in relation to different cognitive components using several modified versions of the WCST (Lie et al., 2006; Monchi et al., 2001). The present study is among the first to use fMRI to investigate direct neural processing in relation to executive function in persons exposed to lead. Therefore, this study aimed to elucidate the possible differences in the neural correlates of ongoing executive function between participants with past lead exposure and healthy controls. We employed two modified versions of the WCST, which differed in cognitive demand, and a high-level baseline condition (HLB) to compare neural processing between the two groups as a function of task complexity (Lie et al., 2006). Based on previous findings of lead-induced executive dysfunction (Canfield et al., 2004; Ris et al., 2004; Sanders et al., 2009; Trope et al., 2001), we hypothesized that participants with past lead exposure would show abnormal activity in distributed neural networks related to executive function during performance of the WCST relative to healthy controls. In particular, we hypothesized that a difference in neural processing related to executive function would be seen in the prefrontal cortex under conditions of high cognitive demand. Therefore, the difference would be seen with a difficult version of the WCST rather than with a simple version. 2. Methods 2.1. Participants Of the 53 recruited subjects previously exposed to lead, 43 were women; therefore, only women were enrolled to eliminate sex as an important effect modifier. The fMRI data from 84 right handed participants, comprising 43 participants with past lead exposure (all female; mean age 60.1  4.9) and 41 age-matched control participants (all female; mean age 58.3  5.2) were included in the current study. We recruited retired former lead workers who had worked in plants producing lead batteries. Control participants were manual workers not exposed to lead or solvents, in other factories in

the same geographic area in Korea. For cultural reasons, lead-based paints have never been used in homes in Korea; thus, all participants reported little or no exposure to lead-based paint residues. All participants had normal vision and had Korean as a first language. Study participants were recruited voluntarily and screened for the presence of any chronic medical illness or disorder. Participants were provided with $150 each for participation in the study as compensation for not being able to work for the several hours required for the examination and travel. All participants in the current fMRI study gave written, informed consent and the local Institutional Review Board approved the study protocols. 2.2. Determination of lead in whole blood Blood lead content was measured in duplicate with a Zeeman background-corrected atomic absorption spectrophotometer (modelZ-8100; Hitachi, Tokyo, Japan) using the standard addition method of the National Institute of Occupational Safety and Health. Blood samples were diluted 1:10 with 1% Triton X-100 in distilled water using 0.5% ammonium phosphate as a modifier, and 15-mL aliquots of the diluted samples were injected onto the platform of the furnace (Kneip, 1988). All blood lead analyses were carried out by the Institute of Environmental and Occupational Medicine, Soonchunhyang University, a laboratory certified by the Korean Ministry of Labor. Since 1988, the institute has served as a reference laboratory for blood lead assessment in a Korean quality control and assurance program. It is licensed by the Ministry of Labor as a uniquely designated institute for nationwide occupational health services to lead-using industries. For the internal quality assurance and control aspects of our study, commercial reference materials were obtained from Bio-Rad (Lyphochek1 Whole Blood Metals Control). The detection limit of the presently used method for blood lead determination was 0.60 mg/dL. No sample contained levels below the detection limit. 2.3. Clinical laboratories The level of hemoglobin was determined using the cyanmet hemoglobin method (Beckman Coulter Inc., model Ac-T 8, United States), and hematocrit was measured using the capillary centrifugation method (Thomas and Collins, 1982). The level of zinc protoporphyrin (ZPP) was measured by a hematofluorometer (Aviv, United States) (Blumberg et al., 1977). The urinary amino levulinic acid (ALA) levels were determined according to the method of Tomokuni et al. (1992). Creatinine levels in urine were analyzed using the appropriate Sigma kit (St. Louis, Missouri, United States) (Heinegard and Tiderstrom, 1973). 2.4. Experimental paradigm A modified version of the WCST was used as the experimental paradigm, with two different test variants (A and B) and a highlevel baseline condition (HLB) in an efficient blocked design (Konishi et al., 2008; Lie et al., 2006). Task A was the most similar to the original Wisconsin Card Sorting Test (WCST) of those used in our experimental task. Participants were first given the control cue word ‘‘card’’ devoid of any information about the current sorting dimension. Participants were asked to identify the subsequent dimension, whether ‘shape’, ‘color’ or ‘number’, by trial and error and the use of feedback stimuli. Because each task A block consisted of 12 trials, a sorting dimension shift occurred once every six trials, resulting in one sorting dimension shift per task A block. As there were 3 task A blocks per experimental run and 3 sorting dimension shifts were experienced in task A blocks: the first shift (shape to number), the second shift (color to number), and the third shift (shape to color).

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In task B, participants were instructed only about the presence of sorting dimension shifts, and were presented with the cue word ‘‘shape’’, ‘‘color’’, or ‘‘number’’ in Korean in the visual field whenever a shift in sorting dimension occurred. After showing the cue word giving the sorting criterion, another cue word, ‘‘card’’, was displayed at the beginning of each trial and during the following three trials. The informed sorting dimension had to be maintained in memory during the three subsequent uninstructed trials until the next sorting dimension shift occurred. There were 2 sorting dimension shifts in each task B block, comprising shifts from shape to color, and from color to number. Shifts occurred 6 times during the experimental run in task B blocks. The high-level baseline (HLB) condition was started by presentation of the cue word ‘‘An identical card’’ for each trial and then volunteers were asked to match the stimulus card to one of the four reference cards without any sorting dimension shift. The HLB condition, the least cognitively demanding process in the experimental blocks, was used as a control condition for visuoperceptual, motor, and cognitive components of the simple selection procedures inherent in the WCST. In the task conditions (A and B), the reference card was different from the stimulus card, whereas in the HLB they were the same. When participants performed the tasks, they watched a binocular, goggle-based system (modified Silent Vision Model SV-7021 Fiber Optic Visual System, Avotec Inc., Stuart, FL) mounted on the head coil, and used a magnet-compatible 4-button box containing fiberoptic switches to relay their responses to the experimenters. One red triangle, two green stars, three yellow crosses, and four blue circles were shown on the four reference cards that were continuously displayed at the top of the visual field. One of the pool of stimulus cards, which consisted of 60 different stimulus cards with all possible permutations of shape (triangle, star, cross, circle), color (red, green, yellow, blue) and number of objects (1, 2, 3, 4), excluding any cards identical to a reference card, was depicted at the bottom of the visual field in a random order. Volunteers were asked to press one of the four buttons using their right fingers to match the stimulus card to 1 of the 4 reference cards based on the sorting dimension (shape, color, or number) they believe correct. All materials and instructions were written in Korean. SuperLab Version 4.5 (Cedrus Corp., San Pedro, CA) controlled presentation of stimuli. When SuperLab received the MRI scan trigger signal, the modified version of the WCST task began immediately. The instruction/cue word (i.e., ‘‘card’’, ‘‘shape’’, ‘‘color’’, ‘‘number’’, or ‘‘an identical card’’) was displayed for 650 ms with an inter-stimulus interval (ISI) of 100 ms. After the cue word ‘‘card’’, the stimulus card was displayed for 3500 ms and then the participant pressed a button to express their own best guess as to the correct matching criterion. After making their selection, participants received a feedback stimulus (a green ‘‘O’’ or a red ‘‘X’’), which was displayed on the screen for 450 ms with an ISI of 50 ms. Therefore, the duration of each trial was 4750 ms (Fig. 1). The stimulus and reference cards were displayed on a visual field of height 21.17 cm and length 28.22 cm. Each card was 7.06 cm high and 4.41 cm long. The distance between the reference cards was 2.12 cm horizontally and between the reference cards and the stimulus card was 3.35 cm vertically. The experiment used a blocked design composed of 3 blocks of task A, 3 blocks of task B and 7 blocks of HLB, switching from one task to another in a fixed sequence (HLB, A, HLB, B, HLB, A, HLB, B, HLB, A, HLB, B, HLB) that started and ended with an HLB block. Each block consisted of 12 successive trials lasting 57 s. 2.5. Data acquisition All MRI data were acquired with a 3.0-T MR scanner (HD, General Electric Healthcare) using a transmit-receive body coil and

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an eight-element head coil array. A 3D-fast spoiled gradient echo sequence (repetition time [TR] = 7.8 ms; echo time [TE] = 3 ms; inversion time 450 ms; flip angle = 20; matrix = 256  256; field of view [FOV] = 24 mm; 1.3 mm thickness, number of slabs = 112, scan time = 5 min) was used for acquisition of T1-weighted highresolution anatomical images. A gradient-echo planar-imaging sequence (TR = 3000 ms; TE = 30 ms; flip angle = 90; matrix = 64  64; FOV = 21 mm; 4 mm thickness, number of slices = 28) was used for acquisition of fMRI with whole-brain coverage. During the experimental run of 741 s, whole-brain data sets of 247 volumes were acquired.

3. Data analysis 3.1. Behavioral Each participant performed the experimental paradigm during the fMRI scan. During the task, the participants viewed the visual stimuli and pressed a magnet-compatible button. When participants were performing the experimental paradigm in the scanner, behavioral results were recorded and then processed to give mean reaction time  standard deviation (SD). Reaction times and the percentage of correct answers (accuracy rate parameters) were analyzed using two-sample t-tests comparing lead-exposure and control groups. The mean reaction times of each task (A-HLB, B-HLB) is defined as the mean reaction times of A or B minus the mean reaction time of the HLB. We determined accuracy rates using the percentage of correct answers of each participant. We saw various error types such as perseverative errors, set-loss errors, and other errors as described in the original WCST. However, our experimental design made it difficult to unambiguously classify the different error types, such as loss of set errors, number of dimensional changes, and number of dimensions, because a sorting dimension shift occurred every six trials in our modified version of the WCST. In the original WCST, ‘loss of set errors’ was defined as errors occurring after 3 correct consecutive answers, and the ‘number of dimensional changes’ and ‘number of dimensions found’ were defined as the number of runs with four correct answers. Therefore, we measured only total correct answers in each block irrespective of error type. The accuracy rate (the percentage of correct answers in each task (A-HLB, B-HLB)) was defined as the percentage of correct answers during the HLB condition minus the percentage of correct answers during either task A or B. 3.2. Processing and statistical analysis of imaging data Image preprocessing and statistical analyses of fMRI data were carried out with the statistical parametric mapping software SPM8 (http://www.fil.ion.ucl.ac.uk/spm/), implemented in MATLAB R2011a (MathWorks, Inc., Sherborn, MA, USA). Functional images were preprocessed through the sequence: slice-timing, realignment, normalization into the Montreal Neurological Institute (MNI) template based on the standard stereotaxic coordinate system, and spatial smoothing with a Gaussian kernel. The kernel had a full width at half-maximum of 8 mm. The fMRI data were superimposed in MNI space. Statistical analysis of fMRI data used a high-pass filter of 128 s to remove low-frequency drifts in signal and was based on the voxel-by-voxel method. A design matrix that modeled the different conditions as reference waveforms was applied, employing a boxcar function convolved with the hemodynamic response function and a least-squares estimation based on the general linear model theory. Motion parameters, including parameters from the realignment procedure that accounted for translational and rotational

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Fig. 1. Schematic diagram of the experimental design, showing views of the experimental set-up of each task condition (A and B) and high-level baseline conditions (HLB). Each block consisted of 12 trials. Red arrows indicate the button response given by the subject. Blocks of task conditions (A or B) were alternated with blocks of HLB conditions. In task A, the stimulus card was preceded by the neutral word ‘card’ on the screen, giving no information about the sorting dimension, and subjects had to deduce the criterion by trial and error and the appropriate use of feedback. In task B, however, the stimulus card provided information only about a change in the sorting criterion, showing the word ‘shape’, ‘color’ or ‘number’ on the screen whenever a change occurred. In the HLB condition, subjects had to choose the card identical to one of the four reference cards. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

head movements, were entered into the statistical analysis as regressors. Contrast images for the effect of each task (A-HLB or B-HLB) were calculated for each participant and for each task. Secondlevel group analysis with a 2  3 full factorial model (2 groups  3 conditions) was used to find the effect of betweencontrast differences after controlling the effect of education level. To obtain the brain activation patterns within groups, we performed post hoc tests in two experimental tasks (A-HLB and BHLB) at a threshold of P <0.05 corrected for false discovery rate (FDR), with a minimum cluster size of 32. To assess group-by-task interactions, contrast images were created for the following contrasts: [control (A-HLB) > lead (A-HLB)] and [control (BHLB) > lead (B-HLB)], and vice versa. Thresholds were set at P < 0.05, FDR-corrected for multiple comparisons at the voxel level, and a minimum cluster extent of 32 contiguous voxels. The MarsBar (http://marsbar.sourceforge.net) ROI tool for SPM was used to identify functional region of interest (ROI) and to perform statistical analyses of ROI. Functional ROI (spheres of 5mm radius: dorsolateral prefrontal cortex; DLPFC) were identified on the basis of the activation voxels for the A-HLB comparisons and were used to extract the mean percent signal change of each of task A and HLB in all participants with lead exposure. With the MarsBar approach, percent signal change in a given ROI is defined as the maximum height of the time course of activation estimated under a single specific condition, divided by the average activity within the ROI determined functionally from the whole-brain analyses, multiplied by 100. The percent signal change calculated as the effect size of A and HLB conditions were defined as the beta value for the mean column of the regression analysis. The percent signal change of the A-HLB task was calculated as the extracted percent signal change of the A condition minus extracted percent signal

change of the HLB condition. We investigated possible correlations of the percent signal change of the A-HLB task with log blood lead concentration. 3.3. Statistical analysis We examined the univariate distributions of the continuous variables to determine their normality. To decrease skewness, some variables were transformed to their natural logarithms and presented as geometric mean (GM) and range; otherwise, arithmetic means (AM) and standard deviations (SD) were used. Mean values of continuous variables were compared using the Student’s t-test. Possible significant intergroup differences in the proportion of workers who smoked or in educational levels were investigated using the Chi-square test. The mean reaction times and the percentage of correct answers between lead-exposure and control groups were analyzed by Student’s t-test using the Statistical Package for the Social Sciences (SPSS) software, version 19 (IBM corp., Armonk, NY, USA). In the present study, we compared the behavioral results between the two groups in several ways. The mean reaction times in each condition (A, B, and HLB) and that of conditions A or B minus the mean reaction time of condition HLB were compared between the two groups using Student’s t-test. The percentage of correct answers in each condition (A, B, and HLB) and the percentage of correct answers in the condition HLB minus the percentage of correct answers in conditions A or B, were also evaluated for intergroup differences using Student’s t-test. We looked for an association of percent BOLD signal change with log blood lead concentration by multiple regression analysis, after controlling for the effects of age, educational level, smoking amount, BMI, and hemoglobin, all of which were chosen a priori.

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The probability threshold of the correlation analysis was set at P < 0.05. 4. Results 4.1. General characteristics The demographic, clinical, and laboratory characteristics of the 43 lead-exposed workers and the 41 control individuals are listed in Table 1. All of the participants were women, and there was no significant age difference between the lead-exposed workers and the control workers. There were no significant differences between the groups in terms of educational levels, smoking status, hemoglobin levels, hematocrit levels, ZPP levels, or urinary ALA levels. There was no significant difference between the two groups in percentage consuming alcohol. The mean blood lead concentration was significantly higher in the lead-exposed workers than in the control group. Among the lead-exposed workers, the duration of lead exposure was 7.9 (1.4–20.7) years, and the time since the cessation of exposure was 11.6 (1.5–25.0) years. Lead-exposed workers showed higher blood lead levels than the control group, even though they had not been exposed to lead for a long time. 4.2. Behavioral data The mean reaction times were highest for task A, and declined with decreasing task complexity (i.e., A > B > HLB) in both leadexposure and control participants, as expected. The two-sample ttest demonstrated a significant group effect on mean reaction times for each condition (A, B, or HLB) and for task (A-HLB). The averages of mean reaction times across all three conditions and for task A-HLB both showed a significant group effect (P < 0.05). Table 1 Demographic, clinical, and laboratory characteristics of participants..

Age (years) Smoking, N (%) Current smoker Ex-smoker Non-smoker Alcohol consumption, N (%) Consumer Non-consumer Education level, N (%) Elementary school or less Middle school High school or more Duration of exposure (years)* Years since cessation of exposure* Blood lead level (mg/dL)* Zinc protoporphyrin (mg/dL) Urinary ALA (mg/L) Hematocrit (%) Hemoglobin (g/dL) Task performance Mean reaction times (ms) A-HLB B-HLB Task accuracy (%) A-HLB B-HLB

Lead-exposed group (N = 43)

Control group (N = 41)

P-value

60.1  4.94

58.3  5.21

0.112 1.000

0 (0%) 1 (2.3%) 42 (97.7%)

0 (0%) 1 (2.4%) 40 (97.6%)

10 (23.3%) 33 (76.7)

7 (17.1%) 34 (82.9%)

29 (67.4%) 12 (27.9%) 2 (4.7%) 7.9 (1.42–20.67) /10.2  6.4 11.6 (1.50–25.00) /12.8  4.9 4.47 (0.88–17.82) 60.5  12.64 2.46  1.017 39.5  2.43 13.0  0.87

20 (48.8%) 13 (31.7%) 8 (19.5%)

0.665

0.072

2.30 (1.27–6.27)

<0.001

62.5  20.64 2.17  0.870 38.5  2.71 12.8  0.91

0.569 0.168 0.073 0.166

769.2  28 661.5  25

933.1  23 713.2  26

<0.01 0.358

40.61  11.2 27.5  14.3

47.63  11.5 22.1  16.5

<0.05 0.112

Mean  SD. * GM (range); in this case statistical significance was tested with log-transformed variables.

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However, mean reaction time for task B-HLB was not significantly different between lead-exposure and control groups (P = 0.358) (Table 1). To assess executive ability, we measured ‘‘accuracy rate’’ under each condition (A, B, or HLB) and for each task (A-HLB or B-HLB). Significantly lower ‘‘accuracy rates’’ were found in A, B, and HLB conditions and in task A-HLB, in the lead-exposure group. No significant group difference was found in the task B-HLB ‘‘accuracy rate’’ as with a negative finding for mean reaction times in this task. The averages of reaction times and the percentage of correct answers in experimental runs were revealed to be significantly different between lead-exposure and control participants (P < 0.05). 4.3. Imaging data In the whole-brain analysis, no significant clusters appeared for the main effect of group. For the main effect of condition, however, a network of frontal and parietal cortical areas was active in all participants. The comparison of task A with the HLB (A-HLB) revealed the whole network involved in the WCST, which further elucidated cognitive function including complex working memory operations, error detection, feedback-utilization, and sorting-dimension shift. The corresponding activations are composed of a bilateral frontoparietal network including the anterior cingulate cortex, DLPFC, ventrolateral prefrontal cortex, and inferior parietal lobulein control participants. The corresponding activations in participants with lead exposure are similar to the activations in control participants, but with weaker and more localized activation in the frontoparietal network (FDR, P < 0.05, k = 32) (Table 2, Fig. 2). The comparison of task B with the HLB (B-HLB) revealed the neural networks underlying simple working memory operations and related attentional demands, while controlling for response selection, visuo-perceptual, motor, and cognitive components. The contrast B-HLB further revealed networks for maintenance and retrieval of the instructed sorting-dimension shift. The activations of the contrast B-HLB are composed of the same networks as in AHLB, but with weaker and more localized activation than the contrast A-HLB mainly in the whole frontoparietal network in both groups (FDR, P < 0.05, k = 32) (Table 3, Fig. 3). Increased task complexity led to a successive enlargement and strengthening of neural activation in the frontoparietal network in both groups. However, control participants showed greater and larger activations than participants with lead exposure only in the contrast of task A with HLB. Contrasting task A with the high-level baseline (A-HLB) revealed significantly increased activations in the left DLPFC (x = 36, y = 6, z = 49) in control participants compared with participants with lead exposure, while contrasting task B with the high-level baseline did not bring out any differences between the two groups (FDR, P < 0.05, k = 32) (Fig. 4(a)). A-HLB showed a moderate correlation with the log blood lead concentration (R = 0.408, P < 0.05) (Fig. 4(b)). We assessed an association of A-HLB with the log blood lead concentration by multiple regression analysis, after controlling for age, educational level, smoking amount, BMI, and hemoglobin level. A-HLB was inversely associated with the log blood lead concentration. Total reaction time on the HLB task was positively associated with the log blood lead concentration (Table 4). 5. Discussion The blood lead concentration of workers with past exposure to lead (GM = 4.47 mg/dL) was higher than that of the control group (GM = 2.30 mg/dL), which was similar to the concentrations found in the general female population of Korea (GM = 2.01 mg/dL) (Kim

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Table 2 Regions of activation from within-group analysis during A-HLB at P < 0.05, FDR-corrected for multiple comparisons in both groups. Group

Region

Subjects with lead exposure

Supplementary motor area

Cluster size

Coordinates (mm) x

Dorsolateral prefrontal cortex Orbitofrontal cotex Pallidum Putamen Thalamus Inferior parietal cortex Cerebellum Healthy controls

Supplementary motor area Dorsolateral prefrontal cortex Orbitofrontal cotex Pallidum Putamen Thalamus Inferior parietal cortex Cerebellum

L R L R L R L R L L R L R L R L R L R L R L R L R L R L R L R

and Lee, 2012). Since the study participants (ex-workers) had not been exposed to lead for approximately 10 years, the higher levels of lead in whole blood of former workers likely reflects ongoing resorption of lead from deeper physiological depots such as bone (Abadin et al., 2007; Sanders et al., 2009). Thus the GM of blood lead concentrations found here, 4.47 mg/dL, suggests a high accumulation of lead in the body. To date, the WCST during fMRI has never been used to examine executive function in participants with past lead exposure. This is among the first study to investigate the difference in neural

724 459 1458 1709 456 225 161 132 106 157 137 157 137 428 591 1166 803 2340 3073 523 728 192 143 266 114 694 587 2080 1397 1188 1349

y 2 2 28 36 46 30 16 18 22 14 14 14 14 44 38 2 4 38 38 30 30 16 20 22 22 10 18 30 36 38 28

Peak T z

20 18 2 6 40 26 0 0 2 10 12 10 12 56 72 22 22 6 12 24 24 6 0 8 8 18 16 66 64 52 56

52 52 56 60 8 6 4 0 4 0 0 0 0 26 28 50 50 54 46 8 8 0 0 0 0 4 12 40 40 22 16

6.54 6.53 5.17 4.20 5.66 3.94 4.04 3.97 2.81 3.04 3.11 3.04 3.11 5.65 4.79 10.10 9.24 9.92 7.54 7.57 7.24 5.26 3.49 3.43 2.72 3.99 3.17 10.23 9.15 7.37 6.43

processing of executive function between participants with past lead exposure and healthy controls. When contrasting task A with HLB (A-HLB), participants with lead exposure showed less activation in distributed cortical networks mediating executive function, particularly prefrontal areas (left DLPFC), than did healthy controls. The percentage changes in the BOLD signals in the left DLPFC of the A-HLB map were inversely associated with the log blood lead concentration after controlling for age, educational level, smoking amount, BMI, and hemoglobin level. This result is in line with prior studies

Fig. 2. Brain activations evoked by the contrast the task A vs. HLB condition in subjects with lead exposure (left) and healthy controls (right).

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Table 3 Regions of activation from within-group analysis during B-HLB at P < 0.05, FDR-corrected for multiple comparisons in both groups. Group

Region

Subjects with lead exposure

Supplementary motor area

Cluster size

Coordinates (mm) x

Dorsolateral prefrontal cortex Orbitofrontal cotex Inferior parietal cortex Cerebellum Healthy controls

Supplementary motor area Dorsolateral prefrontal cortex Orbitofrontal cotex Thalamus Inferior parietal cortex Cerebellum

L R L R L R L R L R L R L R L R L R L R L R

demonstrating abnormalities of prefrontal cortex in participants with lead exposure (Trope et al., 2001; Yuan et al., 2006). Leadexposed children showed reductions in the N-acetylaspartate-tocreatine and phosphocreatine ratios in the frontal gray matter, suggesting increased neuronal loss (Trope et al., 2001). Young adults with a history of greater lead exposure in early childhood showed lower activation in distributed cortical networks, especially in the left frontal cortex, during a verb generation task (Yuan et al., 2006). Therefore, our findings provide further evidence that lead can induce neurotoxic effects in prefrontal areas. Toxic effects of lead in brain occur when lead exposure alters normal functioning and induces damage to the CNS. Lead induced neurotoxicity involves complex mechanisms, especially impairments in neurotransmission. Glutamate is of particular interest because dysfunction in glutamatergic transmission could lead to abnormalities in development, neuronal plasticity, and memory (Neal and Guilarte, 2010; Toscano and Guilarte, 2005). In addition, impairment of dopaminergic systems could result in cognitive and

90 184 404 505 129 69 1121 949 326 73 929 517 1785 2445 256 415 493 431 2244 1366 806 931

y 2 4 44 52 46 32 30 34 46 42 2 4 38 36 30 30 10 14 32 36 46 44

Peak T z

26 22 8 24 42 50 70 62 60 58 20 20 4 4 26 26 16 14 60 64 64 66

48 48 28 30 10 12 38 42 22 -22 54 54 54 58 8 6 0 2 40 44 20 18

3.94 3.47 5.74 5.07 3.92 3.20 8.22 5.66 4.75 3.12 5.93 5.33 7.86 6.04 5.05 5.84 4.25 4.22 8.92 7.10 6.03 5.56

behavioral problems such as attentional deficits, executive dysfunction, and violent behavior (Sanders et al., 2009). Thus, functional abnormalities of the prefrontal cortex in subjects with lead exposure may be attributable to impairments in glutamatergic and dopaminergic transmission caused by lead induced neurotoxicity. Within the prefrontal cortex, healthy controls showed higher activation in the left dorsolateral prefrontal than subjects with lead exposure, when contrasting A-HLB. During the WCST, participants are asked to match randomly drawn cards to reference cards according to a sorting rule, maintain this rule in memory, update feedback information, and shift to a new sorting rule. Thus, the WCST exercises a variety of cognitive functions such as working memory, set shifting, and error detection. Nowadays, it is suggested that there is a functional segregation within the prefrontal cortex, although the prefrontal cortex plays a critical role in mediating these executive functions. The DLPFC, for example, is thought to mediate complex working memory such as monitoring and manipulation of

Fig. 3. Brain activations evoked by the contrast the task B vs. HLB condition in subjects with lead exposure (left) and healthy controls (right).

J. Seo et al. / NeuroToxicology 50 (2015) 1–9

8

Fig. 4. Group differences illustrated in brain activation evoked by the contrast the task A vs. HLB condition (a). Correlation between BOLD signal change and blood lead level with log scale (b).

working memory content and determining response shifting. Thus, functional abnormalities of the prefrontal cortex in participants with lead exposure could be associated with impairments of working memory and set shifting in real life. Most early studies related to lead neurotoxicity have concentrated on general cognitive impairments such as intellectual functioning (Sanders et al., 2009; Weisskopf et al., 2007), and verbal learning and memory (Bleecker et al., 2005). Recently, it has been demonstrated that lead exposure can be associated with executive dysfunction (Canfield et al., 2004; Sanders et al., 2009; Trope et al., 2001). Thus, functional abnormalities of the prefrontal cortex in participants with lead exposure could be related to executive dysfunction, which may result in impairments of higherorder cognitive processes including planning, cognitive flexibility, and abstract reasoning. Our study has some limitations. First, we lacked information about our participants’ past exposure to lead. Participants in the present study were former factory workers occupationally exposed to lead. The lack of information on prior lead exposure may limit the further interpretation of our results, especially with application to aged persons. Further study of bone lead levels with X-ray fluorescence methods, which reflect longer-term cumulative exposure, will be needed (Abadin et al., 2007). Study of active leadexposed workers compared with retired workers is also needed. Second, although the present cross-sectional study showed an association between brain activation and lead exposure, we cannot establish a causal relationship with a cross-sectional design. An unknown third factor might have been a common link responsible for an observed association. Instead, we adjusted for age, educational level, smoking amount, BMI, and hemoglobin level in multiple regression models. A prospective study will be needed to confirm a causal relation. Third, the number of study participants recruited was too small to support a firm conclusion, leading to an

insignificant association of lead exposure with brain signal changes. Small sample size also limited enrollment to women to eliminate sex as an important effect modifier, since lead-related outcomes have been reported to differ between men and women (Cecil et al., 2008). Thus, these results may not be generalized to men. Finally, information on the environmental lead exposure of our participants, such as lead in water, air, or old houses or buildings, such as lead in water, air, or old houses or buildings; child and adult exposure to leaded gasoline; and other potential dietary sources, was not available. The two groups of participants came from the same geographic area of Korea, and for cultural reasons, lead-based paints have never been used in homes in Korea. Therefore, environmental lead exposure was considered similar between the two groups. Taken together, our results warrant further study of executive brain networks not only in retired lead workers but also in active lead workers, and with a larger sample size. In summary, this study aimed to examine the differences in the neural activations underlying executive function between participants with past lead exposure and healthy controls, using fMRI. To this end, we employed two modified versions of the WCST differing in cognitive demand (‘‘A’’, hard; and ‘‘B’’, easy), and a HLB condition. When contrasting both versions of the WCST with the high-level baseline task, participants with lead exposure showed lower activation of the left dorsolateral prefrontal cortex than did healthy controls. The contrast A-HLB was also inversely associated with the log blood lead concentration after controlling for covariates. These data suggest that lead-induced neurotoxicity could result in functional abnormalities in distributed cortical areas involved in executive function. Therefore, these data indicate that lead-induced neurotoxicity may be persistent rather than transient. That is, the present study has the toxicological implication that a long time after lead exposure (11.6 years), some executive function deficits persist (Khalil et al., 2009;

Table 4 Multiple regression for concentration of log blood lead with brain activation after control of age, educational level, smoking amount, BMI, and hemoglobin level. Blood lead concentration

BOLD signal change of left DLPFC in A-HLB Total reaction time in HLB

Model

B (95% CI)

P-value

R2

P-value

0.126 ( 0.19 to 0.063) 121.871 (5.287 to 238.455)

<0.001 0.041

0.137 0.199

0.007 0.001

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Schwartz et al., 2000). Past lead exposure can indeed have a longterm neurotoxic effect, notably because of delayed release from the bones (Schwartz et al., 2000; Todd et al., 2001). Acknowledgments This work was supported by Mid-career Researcher Program through NRF grant funded by the MEST (No. 2010-0004920). This research was also financially supported by the ‘‘Over regional linked 3D convergence industry promotion program’’ through the Ministry of Trade, Industry and Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT).

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