NeuroToxicology 50 (2015) 1–9
Contents lists available at ScienceDirect
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.,
2
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).
J. Seo et al. / NeuroToxicology 50 (2015) 1–9
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
3
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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J. Seo et al. / NeuroToxicology 50 (2015) 1–9
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 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.
J. Seo et al. / NeuroToxicology 50 (2015) 1–9
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)
Contents lists available at ScienceDirect
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.,
2
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 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)
