TY - JOUR
AU1 - Lasky, Robert E.
AU2 - Luck, Melissa L.
AU3 - Parikh, Nehal A.
AU4 - Laughlin, Nellie K.
AB - Abstract Little is known about direct effects of exposure to lead on central nervous system development. We conducted volumetric MRI studies in three groups of 17-year-old rhesus monkeys: (1) a group exposed to lead throughout gestation (n = 3), (2) a group exposed to lead through breast milk from birth to weaning (n = 4), and (3) a group not exposed to lead (n = 8). All fifteen monkeys were treated essentially identically since birth with the exception of lead exposure. The three-dimensional MRI images were segmented on a computer workstation using pre-tested manual and semi-automated algorithms to generate brain volumes for white matter, gray matter, cerebrospinal fluid, and component brain structures. The three groups differed significantly in the adjusted (for total brain size) volumes of the right cerebral white matter and the lateral ventricles. A significant reduction was noted in right cerebral white matter in prenatally exposed monkeys as compared to controls (p = 0.045). A similar reduction was detected in the white matter of the contralateral hemisphere; however, this difference did not achieve statistical significance (p = 0.143). Prenatally exposed monkeys also had larger right (p = 0.027) and left (p = 0.040) lateral ventricles. Depending on the timing of exposure during development, lead may exhibit differential effects with resultant life-long alterations in brain architecture. lead, rhesus monkey, segmentation, volumetric MRI Since the early 1990s there has been a steady decline in body lead burdens in children in the U.S. (Stephenson, 2003). This encouraging news has been tempered by two recent independent reports that blood lead levels below those previously considered to have no adverse consequences are associated with reduced IQ at three and five years of age (Bellinger and Needleman, 2003; Canfield et al., 2003). It is now hypothesized that there is no safe body lead burden (Needleman and Landrigan, 2004); rather, lead produces a continuum of effects, from subtle impairments in cognitive function and behavioral abnormalities at low burdens to encephalopathy and death at high burdens. Our understanding of what structures in the brain are affected by lead is rudimentary. Technological innovations in imaging have dramatically improved our ability to non-invasively assess brain structure and function (Ashburner and Friston, 2000; Fischl et al., 2002; Iosifescu et al., 1997; Smith et al., 2002; Wei et al., 2002). The few neuroimaging reports of lead exposed adults suggest several vulnerable regions in the brain. High level exposure is associated with lesions in the cerebellum, thalamus, putamen, basal ganglia, cerebral cortex, periventricular white matter, and pons on CT and anatomic magnetic resonance imaging (MRI). All of the cases reported in these studies also had significant neurobehavioral symptoms (Mani et al., 1998; Schroter et al., 1991; Teo et al., 1997; Tuzun et al., 2002). The only two published studies concerning the effects of lead toxicity on the brains of children assessed by anatomic MR imaging reported no abnormalities despite blood lead levels ranging from 23 to 65 μg/dl (Trope et al., 1998, 2001). However, there was reduced N-acetylaspartate (a marker of neuronal loss) measured by magnetic resonance spectroscopy (MRS) in the frontal gray matter of these children. Weisskopf et al. reported similar metabolite abnormalities in the frontal lobe as well as the hippocampus and midbrain in a set of adult twins with chronic lead exposure. These patients also had lesions indicative of microinfarcts on MRI (Weisskopf et al., 2004). Subtle central nervous system effects of lead may not be detected by standard neuroimaging evaluation. MRS and other methods such as quantitative volumetric MRI may prove to be more informative in identifying vulnerable brain regions and correlating structural deficits with the neurobehavioral abnormalities associated with lead toxicity. Animal studies offer unique advantages over human studies in evaluating lead toxicity including random allocation to treatment groups and known lead exposure histories of the study participants. The problems of generalizing the results of animal studies to humans are lessened when the animal model is a non-human primate. This is especially true for studies addressing the effects of lead on the brain. There are no comprehensive methodologic or normative 3-D MRI brain volumetric studies on non-human primates. Normative MRI volumetric data for the majority of brain structures in the non-human primate are lacking, and for the rhesus monkey only volumes for the whole brain, amygdala, corpus callosum, thalamus, putamen, and caudate nucleus have been reported (Franklin et al., 2000; Matochik et al., 2000; Schindler et al., 2002). Neuropathology in laboratory animals has documented the adverse effects of lead on microstructural and molecular pathways in the visual area of the occipital lobe (Reuhl et al., 1989), cerebral cortex, hippocampus, and cerebellum among other brain structures (Ma et al., 1997; Reddy et al., 2003; Slomianka et al., 1989; Struzynska et al., 2001). To date, there are no published reports of CT, MRI, or volumetric MRI in lead exposed animals. The present study is an exploratory analysis of the effects of lead exposure in utero and during the immediate postnatal period on regional brain volumes of 17-year-old rhesus monkeys. Little is known about the persistent effects of antenatal and postnatal lead exposure on the mature adult brain. We also describe the reliability of the methods we used to manually and semi-automatically segment various brain structures because of the promise of this methodology in studying the effects of neurotoxins on the brain. Lead induced effects on the anatomy of the brain are of concern if there are associated functional deficits. This study does not address that important association. Rather, it presents a methodology and preliminary data that may prove useful in understanding the structural underpinnings of the functional deficits induced by exposure to lead and other toxins. By identifying affected brain structures, the associated functional deficits can be more efficiently targeted and understood. MATERIALS AND METHODS Subjects. The study cohort consisted of 15 rhesus monkeys (Macaca mulatta) born to adult female monkeys in the spring and summer of 1981 at the University of Wisconsin Harlow Center for Biological Psychology. These monkeys were part of a larger cohort of monkeys participating in a study of the effects of early lead exposure on the auditory system. Details concerning these monkeys, their lead exposure, and lead effects on the auditory system are described elsewhere (Lasky et al., 1995). Eight of these monkeys served as controls. Four of the control monkeys were males, and the other four were females. The other seven monkeys in the study cohort had been exposed to lead either in utero or postnatally until they were weaned. Four of the lead exposed monkeys were males (two prenatally and two postnatally exposed monkeys), and the other three were females (one prenatally and two postnatally exposed monkeys). An eighth lead exposed monkey had a MRI scan of compromised quality due to artifact and was not included in this study because of the inadequate scan. Lead exposed breeding females were administered lead acetate daily in their drinking water beginning three months prior to their time mated pregnancy (8.6 mg/kg body weight per day) and continuing until 5 months postpartum (9.1 mg/kg body weight per day). Details of the lead exposure histories of these monkeys are given in Schantz et al. (1986). All newborn monkeys were cross fostered shortly after birth to recently parturient mothers to create three groups: a prenatal group exposed to lead from conception to birth (n = 3), a postnatal group exposed to lead through breast milk from birth to weaning (n = 4), and a control group whose birth and rearing mothers were never exposed to lead (n = 8). The study monkeys were placed in separate cages with their rearing mothers until weaning at six months postpartum. Weaned monkeys were housed in groups of five until 2 ½ years postpartum after which they were housed separately. The monkeys were treated identically with the exception of their lead exposures. The study monkeys were fed a set diet according to the Harlow Center protocol intended to promote optimal health. With some individual adjustments, males received 12 pieces of monkey chow and females 10 pieces daily. Blood samples from the 15 monkeys in the study sample were obtained by venipuncture biweekly from birth to one year of age, monthly during year two, quarterly until 3½ years postpartum, and periodically thereafter. Four biological maternal blood samples were collected at regular intervals from conception until the birth of their study offspring. Four nursing maternal blood samples were collected at regular intervals from a few days after birth until the weaning their study offspring. All blood samples were analyzed for lead by the Wisconsin State Laboratory of Hygiene by atomic absorption spectroscopy (Model 503, Perkin-Elmer, Boston, MA) using a modified delves cup technique (Ediger and Coleman, 1972). Absorbance was read in duplicate at 283.3 nm with deuterium arc background correction. Average blood lead levels were calculated for each study monkey from birth to weaning and for their biological and nursing mothers during the times they were administered lead. The median maternal blood lead level for the prenatal group during pregnancy was 62.0 μg/dl (minimum = 42.0, maximum = 81.5 μg/dl). The median maternal blood lead level for the postnatal group during nursing was 97.8 μg/dl (minimum = 63.0, maximum = 209.5 μg/dl). The median prenatal offspring blood lead level during nursing was 26.5 μg/dl (minimum = 23.2, maximum = 40.1 μg/dl). The elevated blood lead levels in the prenatal offspring during nursing reflect exposure in utero because there was no further exposure to lead postnatally in these monkeys (i.e., their “postnatal” nursing mothers were never exposed to lead). The median postnatal offspring blood lead level during nursing was 55.1 μg/dl (minimum = 45.5, maximum = 70.8 μg/dl). The median control offspring blood lead level during nursing was 4.5 μg/dl (minimum = 3.4, maximum = 6.3 μg/dl). Blood lead levels for all lead exposed infant monkeys declined after weaning and were <10 μg/dl by 2 ½ years postpartum and <5 μg/dl by 4 ½ years of age. All monkeys (mothers and offspring) were without overt signs of lead toxicity at all times. The monkeys had been in good health since birth and were in good health at the time of the MRI scans for this study. The scans were obtained over a six-month interval from 4 January through 18 June 1999. The median age of the lead-exposed monkeys at the time of the scans was 17.53 years (the range was from 17.48 to 18.14 years). The median age of the control monkeys at the time of the scans was 17.73 years (the range was from 17.61 to 18.16 years). All care and testing of the study cohort was approved by the University of Wisconsin-Madison Animal Care and Use Committee and conformed to the guidelines established by the National Institutes of Health (NIH publication #86-23, 1985). Procedures. The monkeys were housed and studied at the University of Wisconsin-Madison Harlow Center for Biological Psychology. The MRI scanner was located at the Waisman Center (about 2 km from the Harlow Center) on the University of Wisconsin-Madison campus. The monkeys were food deprived the night before they were scanned. Ketamine hydrochloride (Ketaject Phoenix Scientific, Inc., St. Joseph, MO) was given im (10–15 mg/kg) to anesthetize the monkeys for transport by the University Primate Center van. Three members of the research team (R.E.L., M.L.L., and a research assistant) attended to the monkeys in the van and during the entire procedure. At the Waisman Center, the condition of the monkeys was evaluated. An iv line was placed in the monkeys' saphenous vein and a propofol (Diprivan, Stuart Pharmaceuticals, Wilmington, DE) drip was initiated with a 1 ml bolus and a maintenance dose set to effect (no movement of the monkey with stable vital signs). Heart rate, respiration, and SpO2 were continuously monitored before, during, and after the scan (Model 4402, Sensor Devices, Inc., Waukesha, WI). Pre- and post-scan rectal body temperatures were also monitored. The monkeys were placed supine in the scanner. Their heads were positioned upright by a molded foam constraint developed to scan the heads of adult rhesus monkeys. From start to finish the scans took about 45 min. The monkeys were transported back to the Harlow Center and placed in an observation cage overnight in a room adjacent to the larger room housing their home cages. They were returned to their home cages the morning after their scans. None of the monkeys experienced any difficulties. Apparatus. The monkeys were scanned with a GE 1.5 Tesla Oxford Style Magnet with version 5.7 software (General Electric, Milwaukee, WI). A standard GE Quad Head Coil was used. A brief Sagittal T1 localizer scan preceded the Coronal 3D T1 Fast Spoiled Gradient Echo Recall (FSPGR) sequence that was used for the volumetric measurements. The scanning parameters of the 3D T1 FSPGR sequence are listed in Table 1. No post-processing adjustments to reduce movement artifacts and non-uniform intensity levels were employed. TABLE 1 MRI Acquisition Details Parameters   Values used   TR  11.4 ms  TE  2.2 ms  Inversion time  400 ms  Pulse angle  20°  Slice thickness  1.2 mm (Gap = 0)  Slice orientation  Coronal  # of slices  124  Voxel dimensions  0.886 mm3 (width and height = 0.859 mm, depth = 1.200 mm)  Field of view  22 × 16.5 cm  MATRIX  IS: 256 mm AP: 256 mm: LR: (covered head with 124 slices)  NEX   2   Parameters   Values used   TR  11.4 ms  TE  2.2 ms  Inversion time  400 ms  Pulse angle  20°  Slice thickness  1.2 mm (Gap = 0)  Slice orientation  Coronal  # of slices  124  Voxel dimensions  0.886 mm3 (width and height = 0.859 mm, depth = 1.200 mm)  Field of view  22 × 16.5 cm  MATRIX  IS: 256 mm AP: 256 mm: LR: (covered head with 124 slices)  NEX   2   View Large Volumetric measurements. The volumetric measurements were performed by R.E.L. using ANALYZE software (version 4.0, Biomedical Imaging Resource, Mayo Clinic, Rochester, MN). R.E.L. was masked to lead exposure of the study monkeys. Coronal slices were scored sequentially in a posterior to anterior direction. Each scan was scored two times with at least two months separating scoring the same scan. A lengthy training procedure preceded scoring the scans. Primary anatomical references included Paxinos et al. (2000), Martin and Bowden (1996), and Internet accessible monkey atlases (BrainInfo, Comparative Mammalian Brain Collections, and Laboratory of Neuroimaging, UCLA). Local experts were also enlisted to verify the structures measured. It took about 6 h to completely score a single scan. The ANALYZE software provides automated and manual (i.e., outlining structure boundaries by hand) segmentation software tools that were used to segment anatomically based structures (Regions Of Interest or ROIs). Gray scale differences alone did not distinguish all the anatomical structures of interest. Therefore, anatomical landmarks (i.e., the spatial relationships among anatomical structures) in addition to gray scale differences were used to manually segment structures that could not be reliably segmented (automatically) by gray scale thresholding alone. The manually identified structures may combine and blur anatomical distinctions evident with greater resolution and additional information (e.g., different MR protocols, histological, and biochemical data). Consequently, some structures that were scored reflect common anatomical classifications, while others do not. An example of the latter would be structures medial to the cerebrum and superior to the midbrain (we labeled these structures “medial gray matter”). The hippocampus, amygdala, lenticular nucleus, and caudate nucleus could be differentiated by gray scale differences and anatomical landmarks. Medial to those structures, tissues imaged as predominantly gray matter included thalamic structures but also basal ganglia and cerebral structures as well. We could not reliably make those additional distinctions and did not attempt to do so for this study. Table 2 identifies the brain structures scored for this study. It also specifies how those structures were scored. As a result of pre-testing, an invariant procedure was used to score each T1 scan. Figure 1 exemplifies the scoring at several steps in the procedure. The ANALYZE software was used to orient the coronal sections scored so that they were orthogonal to the plane defined by the axis passing through the anterior and posterior commissures and the interaural axis. The structures that could not be reliably scored automatically were manually outlined (using the ANALYZE “Manual Trace” software tool). The left and right hemispheres and the cerebellum were then manually isolated (using the ANALYZE “Auto Trace Limit” software tool) for subsequent automated scoring. Tissues of the head external to the skull were also isolated so that automated scoring did not include those tissues as brain parenchyma or CSF. Prior to scoring, the intensity limits distinguishing gray and white matter and CSF and other opaque tissues were identified from histograms of the voxel intensities of a randomly selected monkey. Cutoffs to distinguish gray matter (56–125), white matter (≥126), and CSF and other tissues (0–55) were selected from the generated histograms and verified by visual inspection of the images (i.e., the cutoffs appeared to correctly distinguish the intended structures on the randomly selected monkey as well as the other monkeys in the dataset). Those intensity limits were then used to segment (using the ANALYZE “Auto Trace” software tool) the cerebral hemispheres and the cerebellum (both gray and white matter) as well as the CSF and skull (scored as “extra parenchymal volume”). FIG. 1. View largeDownload slide An example of scoring an MRI slice. (a) An example of an unscored 3D T1 FSPGR MRI slice, one of the 124 slices acquired on each monkey in this study. (b) The manually identified structures listed in Table 2 were scored using the Analyze “Manual Trace” software tool. (c) After manually identifying structures, predetermined intensity limits were used to identify right and left cerebral white matter and cerebellar white matter automatically using the Analyze “Auto Trace” software tool. The Analyze “Auto Trace Limit” software tool was used to isolate the right and left cerebral hemispheres, the cerebellum, and the brain from the spinal cord. (d) After identifying the cerebral white matter, right and left cerebral gray matter was identified using the predetermined intensity limits and the Analyze “Auto Trace” software tool. Not shown was the automatic identification of “extraparenchymal” tissue (including CSF and skull). The Analyze “Auto Trace Limit” software tool was used to restrict the cerebral gray matter and the extraparenchymal to the brain and skull respectively. After scoring the first slice, the remaining slices were scored in the same manner as described above. These coronal slices were scored in a posterior to anterior direction. When all 124 slices were scored, the ANALYZE software generated volume measurements as well as other statistics for each of the structures identified and listed in Table 2. Those measurements were then compared for monkeys with different exposures to lead. FIG. 1. View largeDownload slide An example of scoring an MRI slice. (a) An example of an unscored 3D T1 FSPGR MRI slice, one of the 124 slices acquired on each monkey in this study. (b) The manually identified structures listed in Table 2 were scored using the Analyze “Manual Trace” software tool. (c) After manually identifying structures, predetermined intensity limits were used to identify right and left cerebral white matter and cerebellar white matter automatically using the Analyze “Auto Trace” software tool. The Analyze “Auto Trace Limit” software tool was used to isolate the right and left cerebral hemispheres, the cerebellum, and the brain from the spinal cord. (d) After identifying the cerebral white matter, right and left cerebral gray matter was identified using the predetermined intensity limits and the Analyze “Auto Trace” software tool. Not shown was the automatic identification of “extraparenchymal” tissue (including CSF and skull). The Analyze “Auto Trace Limit” software tool was used to restrict the cerebral gray matter and the extraparenchymal to the brain and skull respectively. After scoring the first slice, the remaining slices were scored in the same manner as described above. These coronal slices were scored in a posterior to anterior direction. When all 124 slices were scored, the ANALYZE software generated volume measurements as well as other statistics for each of the structures identified and listed in Table 2. Those measurements were then compared for monkeys with different exposures to lead. TABLE 2 Definitions of the Brain Structures Measured Brain structure   Definition   Total brain  Sum of all structures listed in this Table except the ventricles and the extra parenchymal volume.  Cerebrum  Sum of the right and left cerebrums.  Right cerebrum  Sum of the right cerebral gray and white matter.  Left cerebrum  Sum of the left cerebral gray and white matter.  Right cerebral white matter  All structures except the cerebrum and extra parenchymal volume were identified first. The right and left cerebral hemispheres were then segregated manually by bisecting the longitudinal fissure. Cerebral white matter was then automatically identified by the intensity limits for white matter determined from pretesting (the limits that visually separated white from gray from cerebral spinal fluid and other structures).  Left cerebral white matter  The same definition as for right cerebral gray matter.  Right cerebral gray matter  After identifying the right cerebral gray matter, the same procedure was used to identify the right cerebral white matter except the intensity limits identifying white matter were used.  Left cerebral gray matter  The same definition as for right cerebral white matter.  Cerebellum  Sum of the cerebellar gray and white matter.  Cerebellum, white matter  The cerebellum was manually segregated from the rest of the brain. Cerebellar white matter was then automatically identified by the intensity limits for white matter determined from pretesting.  Cerebellum, gray matter  After identifying the right cerebellar white matter the intensity limits identifying gray matter were used to automatically identify the cerebellar gray matter.  Basal ganglia  Sum of the right and left basal ganglia.  Right basal ganglia  Sum of the right caudate, lenticular nucleus, amygdala, and acumbens.  Left basal ganglia  Sum of the left caudate, lenticular nucleus, amygdala, and acumbens.  Right caudate  Gray matter surrounding and extending lateral from the anterior horn of the right lateral ventricle and inferior to the corpus callosum.  Left caudate  Gray matter surrounding and extending lateral from the anterior horn of the left lateral ventricle and inferior to the corpus callosum.  Right lenticular nucleus  Gray matter in the right cerebral hemisphere medial to the insular cortex and the adjacent extreme capsule, inferior and lateral to the right caudate nucleus, lateral to the internal capsule, and superior to the hippocampus, auditory and optic radiations, and the anterior commissure.  Left lenticular nucleus  The same definition as for the right lenticular nucleus.  Right amygdala  Proceeding from posterior to anterior coronal slices, the right amygdala was initially defined as the gray matter superior to the right hippocampus and separated from it by the inferior horn of the right lateral ventricle. It was inferior to the internal capsule, the auditory and optic radiations, the anterior commissure, and the right lenticular nucleus. Proceeding anterior, the right hippocampus and lateral ventricles diminish being replaced entirely by the amygdala (it was the only structure occupying the temporal pole other than the cerebrum).  Left amygdala  The same definition as for the right amygdale.  Right acumbens  Gray matter that connected the inferior right caudate laterally to the superior right lenticular nucleus.  Left acumbens  The same definition as for the right acumbens.  Right hippocampus  First defined in posterior coronal sections as gray matter medial to the posterior horn of the right lateral ventricle. More anterior, it became localized inferior to the posterior and inferior horns of the right lateral ventricle.  Left hippocampus  The same definition as for the right hippocampus.  Diencephalon  Sum of the right and left diencephalons.  Right diencephalon  Sum of the right medial gray matter and the right hypothalamus.  Left diencephalon  Sum of the left medial gray matter and the left hypothalamus.  Right medial gray matter  After segregating the right hemisphere, gray matter lateral to midline and bounded by the internal capsule, the auditory and optic radiations, and the anterior commissure.  Left medial gray matter  The same definition as for the right medial gray matter.  Right hypothalamus  Gray matter in the right hemisphere, anterior to the midbrain, lateral to the midline, and inferior to the thalamus and anterior commissure.  Left hypothalamus  The same definition as for the right hypothalamus.  Brainstem  Sum of the midbrain, pons, and medulla.  Midbrain  First defined in posterior coronal sections by the colliculi, then as the gray matter superior to the pons connected to the thalamus and adjacent hemispheres. The superior extent was defined at midline by the inferior terminus of the third ventricle and by an arc extending from that point to the superior extent of the cerebral spinal fluid separating the midbrain and the right cerebral hemisphere.  Pons  Gray matter between the midbrain and medulla oblongata. The bulging middle cerebellar peduncles define the lateral extent of the pons and the superior and inferior borders defined by the peduncles were used to differentiate the pons from the midbrain and the medulla.  Medulla  Trapezoidal gray mater extending inferior from the pons and connecting to the narrower diameter spinal cord. The inferior boundary was defined manually at the point below which the spinal cord was of constant diameter.  Ventricles  Sum of right and left lateral ventricles, the third and fourth ventricles, and the cerebral aqueduct.  Right lateral  The structure in the intensity range of cerebral spinal fluid (defined visually in pretesting) localized laterally within the parenchyma of the right hemisphere.  Left lateral  The same definition as for the left lateral ventricle.  Third  CSF at midline inferior to the lateral ventricles and superior to the midbrain.  Cerebral aqueduct  CSF at midline within the midbrain.  Fourth  CSF within the pons and medulla oblongata.  Corpus callosum  The structure in the intensity range of white matter (defined visually in pretesting) superior to the lateral ventricles and caudate nuclei and crossing the midline.  Septum  Narrow sliver of medial white matter inferior and orthogonal to the corpus callosum and bounded laterally by the right and left lateral ventricles.  Fornix  First defined in posterior coronal sections as white matter extending inferior from the medial corpus callosum and laterally to the posterior horns of the lateral ventricles. More anteriorally it is defined as the enlarged inferior base extending from the septum.  Optic Chiasm  The juncture of the right and left optic nerves as they join at midline anterior to the midbrain.  Pineal  Small, spherical midline structure surrounded by CSF, superior to the superior colliculi and inferior to the corpus callosum.  Pituitary  Spherical midline structure surrounded by CSF and inferior to the optic chiasm.  Extra parenchymal volume   The head was first segregated manually from the surrounding space. After defining all the other structures listed in this Table, the extra parenchymal tissue was automatically identified by intensity limits determined from pretesting (the limits that visually separated gray and white parenchymal matter from cerebral spinal fluid and other structures constituting the structures defining the extra parenchymal volume).   Brain structure   Definition   Total brain  Sum of all structures listed in this Table except the ventricles and the extra parenchymal volume.  Cerebrum  Sum of the right and left cerebrums.  Right cerebrum  Sum of the right cerebral gray and white matter.  Left cerebrum  Sum of the left cerebral gray and white matter.  Right cerebral white matter  All structures except the cerebrum and extra parenchymal volume were identified first. The right and left cerebral hemispheres were then segregated manually by bisecting the longitudinal fissure. Cerebral white matter was then automatically identified by the intensity limits for white matter determined from pretesting (the limits that visually separated white from gray from cerebral spinal fluid and other structures).  Left cerebral white matter  The same definition as for right cerebral gray matter.  Right cerebral gray matter  After identifying the right cerebral gray matter, the same procedure was used to identify the right cerebral white matter except the intensity limits identifying white matter were used.  Left cerebral gray matter  The same definition as for right cerebral white matter.  Cerebellum  Sum of the cerebellar gray and white matter.  Cerebellum, white matter  The cerebellum was manually segregated from the rest of the brain. Cerebellar white matter was then automatically identified by the intensity limits for white matter determined from pretesting.  Cerebellum, gray matter  After identifying the right cerebellar white matter the intensity limits identifying gray matter were used to automatically identify the cerebellar gray matter.  Basal ganglia  Sum of the right and left basal ganglia.  Right basal ganglia  Sum of the right caudate, lenticular nucleus, amygdala, and acumbens.  Left basal ganglia  Sum of the left caudate, lenticular nucleus, amygdala, and acumbens.  Right caudate  Gray matter surrounding and extending lateral from the anterior horn of the right lateral ventricle and inferior to the corpus callosum.  Left caudate  Gray matter surrounding and extending lateral from the anterior horn of the left lateral ventricle and inferior to the corpus callosum.  Right lenticular nucleus  Gray matter in the right cerebral hemisphere medial to the insular cortex and the adjacent extreme capsule, inferior and lateral to the right caudate nucleus, lateral to the internal capsule, and superior to the hippocampus, auditory and optic radiations, and the anterior commissure.  Left lenticular nucleus  The same definition as for the right lenticular nucleus.  Right amygdala  Proceeding from posterior to anterior coronal slices, the right amygdala was initially defined as the gray matter superior to the right hippocampus and separated from it by the inferior horn of the right lateral ventricle. It was inferior to the internal capsule, the auditory and optic radiations, the anterior commissure, and the right lenticular nucleus. Proceeding anterior, the right hippocampus and lateral ventricles diminish being replaced entirely by the amygdala (it was the only structure occupying the temporal pole other than the cerebrum).  Left amygdala  The same definition as for the right amygdale.  Right acumbens  Gray matter that connected the inferior right caudate laterally to the superior right lenticular nucleus.  Left acumbens  The same definition as for the right acumbens.  Right hippocampus  First defined in posterior coronal sections as gray matter medial to the posterior horn of the right lateral ventricle. More anterior, it became localized inferior to the posterior and inferior horns of the right lateral ventricle.  Left hippocampus  The same definition as for the right hippocampus.  Diencephalon  Sum of the right and left diencephalons.  Right diencephalon  Sum of the right medial gray matter and the right hypothalamus.  Left diencephalon  Sum of the left medial gray matter and the left hypothalamus.  Right medial gray matter  After segregating the right hemisphere, gray matter lateral to midline and bounded by the internal capsule, the auditory and optic radiations, and the anterior commissure.  Left medial gray matter  The same definition as for the right medial gray matter.  Right hypothalamus  Gray matter in the right hemisphere, anterior to the midbrain, lateral to the midline, and inferior to the thalamus and anterior commissure.  Left hypothalamus  The same definition as for the right hypothalamus.  Brainstem  Sum of the midbrain, pons, and medulla.  Midbrain  First defined in posterior coronal sections by the colliculi, then as the gray matter superior to the pons connected to the thalamus and adjacent hemispheres. The superior extent was defined at midline by the inferior terminus of the third ventricle and by an arc extending from that point to the superior extent of the cerebral spinal fluid separating the midbrain and the right cerebral hemisphere.  Pons  Gray matter between the midbrain and medulla oblongata. The bulging middle cerebellar peduncles define the lateral extent of the pons and the superior and inferior borders defined by the peduncles were used to differentiate the pons from the midbrain and the medulla.  Medulla  Trapezoidal gray mater extending inferior from the pons and connecting to the narrower diameter spinal cord. The inferior boundary was defined manually at the point below which the spinal cord was of constant diameter.  Ventricles  Sum of right and left lateral ventricles, the third and fourth ventricles, and the cerebral aqueduct.  Right lateral  The structure in the intensity range of cerebral spinal fluid (defined visually in pretesting) localized laterally within the parenchyma of the right hemisphere.  Left lateral  The same definition as for the left lateral ventricle.  Third  CSF at midline inferior to the lateral ventricles and superior to the midbrain.  Cerebral aqueduct  CSF at midline within the midbrain.  Fourth  CSF within the pons and medulla oblongata.  Corpus callosum  The structure in the intensity range of white matter (defined visually in pretesting) superior to the lateral ventricles and caudate nuclei and crossing the midline.  Septum  Narrow sliver of medial white matter inferior and orthogonal to the corpus callosum and bounded laterally by the right and left lateral ventricles.  Fornix  First defined in posterior coronal sections as white matter extending inferior from the medial corpus callosum and laterally to the posterior horns of the lateral ventricles. More anteriorally it is defined as the enlarged inferior base extending from the septum.  Optic Chiasm  The juncture of the right and left optic nerves as they join at midline anterior to the midbrain.  Pineal  Small, spherical midline structure surrounded by CSF, superior to the superior colliculi and inferior to the corpus callosum.  Pituitary  Spherical midline structure surrounded by CSF and inferior to the optic chiasm.  Extra parenchymal volume   The head was first segregated manually from the surrounding space. After defining all the other structures listed in this Table, the extra parenchymal tissue was automatically identified by intensity limits determined from pretesting (the limits that visually separated gray and white parenchymal matter from cerebral spinal fluid and other structures constituting the structures defining the extra parenchymal volume).   View Large Analyses. The study results are presented in four sections. The first concerns the reliability of making two independent volumetric measurements by the same person (R.E.L.) on the same scan from the same monkey for the brain structures identified in this study. We followed Bland and Altman's (1996) recommendations and used Intra-Class Correlation (ICC) coefficients, within standard deviations (wSDs), and repeatability to characterize measurement reliabilities. The wSDs are the average difference between the two measurements for the entire sample (i.e., the SDs of the repeated measurements). Repeatability is defined as the difference between two measurements as large as 95% of the differences between repeat measurements. The intra-scorer reliabilities for all the structures scored are presented. These reliabilities were calculated for the eight control monkeys. There may be gender differences in the effects of lead on the brain. In the second section gender differences were explored by a 2(gender) × 2(group- lead exposed and controls) analysis of variance. The third section considers the relationships among the brain structures measured. A principal components analysis was not possible given the small sample size. Instead, we calculated Pearson correlation coefficients and partial correlation coefficients (adjusting for total brain volume) to identify brain structures correlated with each other. These analyses also determined whether it was necessary to adjust brain structures by total brain volume because brain structure volumes reflect overall brain size as well as the relative sizes of each individual structure. The final section evaluates lead effects on brain structures. A multivariate analysis of variance (MANOVA) was not possible because we had more dependent variables than monkeys, some of those dependent variables (right and left analogs of the same structure) were highly correlated raising concerns of multicollinearity among the dependent variables, and the sparseness of the data made it difficult to test assumptions of multivariate normality and homogeneity of the covariance matrices. Therefore, we adopted a univariate analytic approach to identify possible lead effects on the structures we measured. We calculated one way analyses of variance (ANOVA) to evaluate the effects of lead exposure (prenatally, postnatally, or no exposure) on total brain volume. We calculated one way analyses of covariance (ANCOVAs) for each of the individual brain structures (ROIs) we measured. The independent variable in these analyses was lead exposure (prenatal, postnatal, or no exposure). The covariate in these analyses was total brain volume. These analyses indicate whether volume differences in specific brain structures were associated with lead exposure (in utero or the early postnatal period) adjusting for total brain volume. Our small sample size dictated that we could reliably detect only very large effect sizes. Accepting a type I error of 0.05, the control monkeys would have to have a brain structure volume 2.85 standard deviations greater than that of the prenatal and the postnatal monkeys to be detected with 80% power by a one-way ANOVA. Because of our limited power to detect even sizeable effects, trends in the data were of interest to avoid missing biologically important effects. Whether to adjust the nominal significance level for multiple comparisons is an important consideration. We scored 36 independent structures (we also combined those structures to form composite structures). The probability that one of the 36 univariate ANCOVAs we calculated would be “significant” at the 0.05 level is 0.84. Nevertheless, we did not adjust for multiple comparisons. Following Rothman's (1990) arguments, we chose to identify patterns in the results and avoid type II errors rather than adjust the type I error rate for multiple comparisons. In exploratory research, identifying real effects is more important than avoiding false positives. Our results must be interpreted with these considerations in mind. The results of the formal tests and diagnostics we conducted indicated that the assumptions made by our parametric testing were not violated, however the power of those formal tests was limited by our small sample size. Because of the small sample size and uncertainty about the distributions of the measurements, non-parametric analyses were conducted to confirm our parametric tests. Both fixed effect parametric and Kruskal-Wallis non-parametric one way ANOVAs were calculated to compare total brain volume of the prenatally lead exposed group, the postnatally lead exposed group, and the controls. We used the residual of the linear regressions of total brain volume on each of the individual component brain structures as the dependent variable in Kruskal-Wallis non-parametric one way ANOVAs. Those ANOVAs are non-parametric analogs of the ANCOVAs we calculated for those same structures. The results of the non-parametric analyses confirmed the parametric analyses and are not reported. Dose response relationships were explored by correlating the blood lead levels of the study monkeys from birth to weaning and from the mothers (during their pregnancies for the prenatal monkeys and while nursing for the postnatal monkeys) with brain structure volumes identified by the ANCOVAs as affected by lead exposure. Both Pearson and Spearman partial correlation coefficients were calculated (partialling out the effect of total brain volume). The calculated parametric and non-parametric partial correlations were similar. We also calculated linear regressions (adjusting for total brain volume) in order to quantify the change in brain structure volumes associated with measured blood lead levels. The analyses were conducted using SPSS (version 10.0.7; June 2000; SPSS, Inc., Chicago, IL. 60606) and NCSS (version NCSS 2004; March 2004; NCSS, Kaysville, UT) statistical software packages. RESULTS Reliability The ICCs and their 95% confidence intervals (C.I.) are presented in Table 3 for each of the brain structures measured. Some of the structures listed in Table 3 are combinations of component structures (e.g., the basal ganglia included the caudate nucleus, the lenticular nucleus, the amygdala, and the acumbens). Table 3 also presents the within standard deviations (wSDs) and repeatability for the brain structures measured. The reliabilities of our measurements were very high; all would be characterized as having excellent reliability. Automatically scored structures (e.g., cerebral gray and white matter) had among the highest reliabilities as expected; however, even manually scored structures with the poorest reliabilities had ICCs greater than 0.77. As scored, the volumetric measurements have little intra-scorer measurement error associated with them. TABLE 3 Reliabilities of the Measured Brain Volumes Brain structure   ICC (95% C.I.)   Within subject SD in mm3 (repeatability in mm3)   Total brain  .9997 (.9987, .9999)  128.08 (354.77)  Cerebrum  .9982 (.9905, .9996)  221.11 (612.47)  Right cerebrum  .9980 (.9906, .9996)  120.24 (333.05)  Left cerebrum  .9919 (.9635, .9984)  223.96 (620.38)  Right cerebral gray matter  .9959 (.8576, .9994)  121.47 (336.47)  Left cerebral gray matter  .9970 (.9792, .9994)  126.85 (351.38)  Right cerebral white matter  .9962 (.9805, .9993)  149.34 (413.67)  Left cerebral white matter  .9913 (.9578, .9983)  163.04 (451.62)  Cerebellum  .9981 (.9908, .9996)  55.10 (152.63)  Cerebellum, gray matter  .9997 (.9987, .9999)  28.69 (79.46)  Cerebellum, white matter  .9997 (.9985, .9999)  35.37 (97.97)  Basal ganglia  .9598 (.8229, .9917)  131.13 (363.22)  Right basal ganglia  .9542 (.7772, .9908)  69.49 (192.47)  Left basal ganglia  .9628 (.8420, .9923)  66.77 (184.95)  Right caudate  .9868 (.9163, .9975)  16.69 (46.23)  Left caudate  .8990 (.6036, .9786)  24.80 (68.71)  Right lenticular nucleus  .8550 (.4789, .9686)  37.11 (102.80)  Left lenticular nucleus  .9447 (.7545, .9886)  33.17 (91.87)  Right amygdala  .9808 (.9069, .9961)  17.79 (49.27)  Left amygdala  .9771 (.8793, .9955)  24.00 (66.48)  Right acumbens  .8386 (.4255, .9649)  10.61 (29.39)  Left acumbens  .9331 (.5801, .9873)  11.48 (31.79)  Right hippocampus  .9540 (.7915, .9906)  17.26 (47.81)  Left hippocampus  .9628 (.8309, .9924)  19.49 (53.99)  Diencephalon  .9308 (.7196, .9855)  48.93 (135.54)  Right diencephalon  .8531 (.4444, .9686)  50.47 (139.81)  Left diencephalon  .8199 (.1779, .9638)  38.49 (106.63)  Right medial gray matter  .8776 (.5043, .9743)  46.04 (127.52)  Left medial gray matter  .7788 (−.0236, .9577)  43.45 (120.35)  Right hypothalamus  .8355 (.4123, .9642)  10.42 (28.86)  Left hypothalamus  .9008 (.4193, .9810)  11.16 (30.91)  Brainstem  .9492 (.7842, .9895)  74.22 (205.60)  Midbrain  .9431 (.7498, .9883)  42.31 (117.21)  Pons  .9718 (.8782, .9942)  25.17 (69.73)  Medulla  .9470 (.7591, .9892)  45.72 (126.65)  Ventricles  .9837 (.9240, .9967)  19.53 (54.09)  Right lateral  .9281 (.7082, .9849)  18.98 (52.58)  Left lateral  .9865 (.9323, .9973)  10.03 (27.78)  Third  .8593 (.4750, .9698)  2.61 (7.24)  Cerebral aqueduct  .9518 (.7801, .9902)  1.72 (4.75)  Fourth  .8244 (.3904, .9615)  9.39 (26.01)  Corpus callosum  .9972 (.9873, .9994)  16.20 (44.86)  Septum  .9224 (.6746, .9839)  3.29 (9.12)  Fornix  .9390 (.7056, .9877)  14.90 (41.28)  Optic Chiasm  .7773 (.2387, .9509)  17.91 (49.61)  Pineal  .9941 (.9735, .9988)  0.66 (1.84)  Pituitary  .9787 (.8981, .9957)  5.15 (14.27)  Extra parenchymal volume   .9966 (.9840, .9993)   347.17 (961.65)   Brain structure   ICC (95% C.I.)   Within subject SD in mm3 (repeatability in mm3)   Total brain  .9997 (.9987, .9999)  128.08 (354.77)  Cerebrum  .9982 (.9905, .9996)  221.11 (612.47)  Right cerebrum  .9980 (.9906, .9996)  120.24 (333.05)  Left cerebrum  .9919 (.9635, .9984)  223.96 (620.38)  Right cerebral gray matter  .9959 (.8576, .9994)  121.47 (336.47)  Left cerebral gray matter  .9970 (.9792, .9994)  126.85 (351.38)  Right cerebral white matter  .9962 (.9805, .9993)  149.34 (413.67)  Left cerebral white matter  .9913 (.9578, .9983)  163.04 (451.62)  Cerebellum  .9981 (.9908, .9996)  55.10 (152.63)  Cerebellum, gray matter  .9997 (.9987, .9999)  28.69 (79.46)  Cerebellum, white matter  .9997 (.9985, .9999)  35.37 (97.97)  Basal ganglia  .9598 (.8229, .9917)  131.13 (363.22)  Right basal ganglia  .9542 (.7772, .9908)  69.49 (192.47)  Left basal ganglia  .9628 (.8420, .9923)  66.77 (184.95)  Right caudate  .9868 (.9163, .9975)  16.69 (46.23)  Left caudate  .8990 (.6036, .9786)  24.80 (68.71)  Right lenticular nucleus  .8550 (.4789, .9686)  37.11 (102.80)  Left lenticular nucleus  .9447 (.7545, .9886)  33.17 (91.87)  Right amygdala  .9808 (.9069, .9961)  17.79 (49.27)  Left amygdala  .9771 (.8793, .9955)  24.00 (66.48)  Right acumbens  .8386 (.4255, .9649)  10.61 (29.39)  Left acumbens  .9331 (.5801, .9873)  11.48 (31.79)  Right hippocampus  .9540 (.7915, .9906)  17.26 (47.81)  Left hippocampus  .9628 (.8309, .9924)  19.49 (53.99)  Diencephalon  .9308 (.7196, .9855)  48.93 (135.54)  Right diencephalon  .8531 (.4444, .9686)  50.47 (139.81)  Left diencephalon  .8199 (.1779, .9638)  38.49 (106.63)  Right medial gray matter  .8776 (.5043, .9743)  46.04 (127.52)  Left medial gray matter  .7788 (−.0236, .9577)  43.45 (120.35)  Right hypothalamus  .8355 (.4123, .9642)  10.42 (28.86)  Left hypothalamus  .9008 (.4193, .9810)  11.16 (30.91)  Brainstem  .9492 (.7842, .9895)  74.22 (205.60)  Midbrain  .9431 (.7498, .9883)  42.31 (117.21)  Pons  .9718 (.8782, .9942)  25.17 (69.73)  Medulla  .9470 (.7591, .9892)  45.72 (126.65)  Ventricles  .9837 (.9240, .9967)  19.53 (54.09)  Right lateral  .9281 (.7082, .9849)  18.98 (52.58)  Left lateral  .9865 (.9323, .9973)  10.03 (27.78)  Third  .8593 (.4750, .9698)  2.61 (7.24)  Cerebral aqueduct  .9518 (.7801, .9902)  1.72 (4.75)  Fourth  .8244 (.3904, .9615)  9.39 (26.01)  Corpus callosum  .9972 (.9873, .9994)  16.20 (44.86)  Septum  .9224 (.6746, .9839)  3.29 (9.12)  Fornix  .9390 (.7056, .9877)  14.90 (41.28)  Optic Chiasm  .7773 (.2387, .9509)  17.91 (49.61)  Pineal  .9941 (.9735, .9988)  0.66 (1.84)  Pituitary  .9787 (.8981, .9957)  5.15 (14.27)  Extra parenchymal volume   .9966 (.9840, .9993)   347.17 (961.65)   View Large Gender Effects Female rhesus monkeys have smaller brains than males (Cupp and Uemura, 1981; Franklin et al., 2000). Gender differences in total brain volume in the study sample were explored by a 2(gender) × 2 (group) analysis of variance. Because of the small sample size, the two lead exposed groups were combined for this analysis. There was a significant gender × group interaction (F(1,11) = 5.22; p = 0.043) for total brain volume, the only significant (p < 0.05) effect. (Male total brain volumes were more variable than female brain volumes violating the homoscedasticity assumption.) Control female monkeys (mean = 100,384 mm3, SD = 4499 mm3) control male monkeys (mean = 97,546 mm3, SD = 12,278 mm3), and lead-exposed male monkeys (mean = 100,906 mm3, SD = 7844 mm3) had similar sized brains. In contrast, lead exposed females had smaller brains (mean = 84,534 mm3, SD = 3,220 mm3) than the other monkeys. These results suggest that the control males had unusually small brains. There is some support for this conjecture because the control males also weighed less than expected. They were similar in body weight to the control females (median body weight for the control males was 7.18 kg, range = 6.75 to 11.10 kg; for the control females it was 8.20 kg, range = 6.80 to 8.50 kg) and significantly lighter than the lead exposed males (median body weight = 12.22 kg, range = 9.95 to 12.90 kg). As expected the lead exposed males were significantly heavier than lead exposed females (median body weight = 6.65 kg, range = 6.40 to 7.50 kg). One implication of these analyses is the need to adjust for differences in total brain volume because of sampling variation in the sizes of the study monkeys. Correlations among Structures Table 4 presents correlations among major brain structures. Total brain volume correlates most strongly with the cerebrum followed by the cerebellum, basal ganglia, brainstem, diencephalon, the ventricles, and is negatively but non-significantly correlated with extra parenchymal volume. Not surprisingly, monkeys with larger brains tended to have larger component structures. Extra parenchymal volume was the only variable not included in total brain volume. The negative correlation with extra parenchymal volume suggests that the larger the brain the less the extra parenchymal volume. TABLE 4 Pearson Correlation Coefficients among Major Brain Structures above the Diagonal and Partial Correlation Coefficients Adjusting for Total Brain Volume Below the Diagonal Brain structure   Cerebrum   Cerebellum   Basal ganglia   Diencephalon   Brainstem   Ventricles   Extra parenchymal   Total brain  .99*  .89*  .87*  .67  .78*  .36  −.48  Cerebrum    .83*  .84*  .64  .75*  .40  −.52  Cerebellum  −.79*    .73*  .68  .70  .18  −.49  Basal ganglia  −.27  −.19    .44  .60  .11  −.09  Diencephalon  −.24  .23  −.40    .68  .55  −.60  Brainstem  −.25  .01  .60  .33    .71*  −.28  Ventricles  .30  −.35  −.45  .45  .74    −.25  Extra parenchymal volume   −.35   −.15   .76*   −.42   .18   −.09     Brain structure   Cerebrum   Cerebellum   Basal ganglia   Diencephalon   Brainstem   Ventricles   Extra parenchymal   Total brain  .99*  .89*  .87*  .67  .78*  .36  −.48  Cerebrum    .83*  .84*  .64  .75*  .40  −.52  Cerebellum  −.79*    .73*  .68  .70  .18  −.49  Basal ganglia  −.27  −.19    .44  .60  .11  −.09  Diencephalon  −.24  .23  −.40    .68  .55  −.60  Brainstem  −.25  .01  .60  .33    .71*  −.28  Ventricles  .30  −.35  −.45  .45  .74    −.25  Extra parenchymal volume   −.35   −.15   .76*   −.42   .18   −.09     * Indicates p < 0.05, two-tailed test. View Large We calculated partial correlations to determine the correlations among brain structures adjusting for total brain volume (i.e., standardizing the sizes of the brain structures scored by the overall size of the brain). Those partial correlations are presented below the diagonal in Table 4. The partial correlation between the cerebrum and cerebellum became negative after adjusting for total brain volume. The partial correlations among the other structures were nonsignificant with a few exceptions (e.g., a significant positive partial correlation between extra parenchymal volume and the basal ganglia). For the cerebrum the white and gray matter was negatively (but nonsignificantly) correlated, while the negative correlation between cerebellar gray and white matter was significant (r = −.786, p = 0.021). Not surprisingly, right and left analogs of the same component structure were also positively correlated for the brain structures scored, significantly so for gray (r = .754, p = 0.031) and white (r = .734, p = 0.038) cerebral matter. Lead Effects Table 5 presents group differences for the brain structures measured. Except for total brain volume, all of the other measurements were adjusted by total brain volume. There were no significant (p < 0.05) differences in total brain volume as a function of lead exposure group by a one way ANOVA. ANCOVAs with total brain volume as the covariate were calculated to evaluate lead effects on individual brain structures adjusted for variation in total brain volumes. Only the lateral ventricles (F(2,11) = 5.105; p = 0.027 for the right lateral ventricle and F(2,11) = 4.389; p = 0.040 for the left lateral ventricle) and cerebral white matter (F(2,11) = 4.158; p = 0.045 for the right cerebral white matter and a similar but non-significant difference for the left cerebral white mater, F(2,11) = 2.331; p = 0.143) significantly differed among the lead exposure groups of monkeys. Monkeys exposed to lead prenatally had the largest lateral ventricles. Control monkeys had more cerebral white matter than lead exposed monkeys. The prenatally exposed monkeys had the least cerebral white matter. Differences in cerebral white matter were similar for both hemispheres, but more consistent for the right cerebral hemisphere. There also tended to be more cerebral gray matter in the brains of lead-exposed monkeys although that trend was not statistically significant. The results and trends in Table 5 are summarized and presented graphically in Figure 2. FIG. 2. View largeDownload slide A summary of adjusted volumetric brain differences among monkeys with different lead exposures. Each pie represents the adjusted brain volume for monkeys with prenatal lead exposure (n = 3), postnatal lead exposure (n = 4), or no exposure to lead (n = 8). Pie slices represent the percentage of the total brain volume that was cerebral white matter (wm), cerebral gray matter (gm), other wm, other gm, or the ventricles. Offset slices include structures (right cerebral wm and the right and left lateral ventricles) that differed significantly (p < 0.05) by univariate ANCOVAs among the three lead exposure groups. FIG. 2. View largeDownload slide A summary of adjusted volumetric brain differences among monkeys with different lead exposures. Each pie represents the adjusted brain volume for monkeys with prenatal lead exposure (n = 3), postnatal lead exposure (n = 4), or no exposure to lead (n = 8). Pie slices represent the percentage of the total brain volume that was cerebral white matter (wm), cerebral gray matter (gm), other wm, other gm, or the ventricles. Offset slices include structures (right cerebral wm and the right and left lateral ventricles) that differed significantly (p < 0.05) by univariate ANCOVAs among the three lead exposure groups. TABLE 5 Differences in Brain Structure Volumes (Mean and 95% C.I. in mm3) Adjusted by Total Brain Volume Among Prenatally Lead Exposed Monkeys, Postnatally Lead Exposed Monkeys, and Monkeys Never Exposed to Lead Brain structure   Prenatal monkeys (n = 3)   Postnatal monkeys (n = 4)   Control monkeys (n = 8)   Total braina  98707 (64051, 133363)  90276 (78885, 101667)  98965 (91697, 106233)  Cerebrum  70415 (69126, 71705)  70666 (69476, 71856)  71680 (70879, 72481)  Right cerebrum  35276 (34327, 36225)  36326 (35451, 37202)  36003 (35414, 36593)  Left cerebrum  35139 (34039, 36240)  34340 (33325, 35355)  35677 (34994, 36361)  Right cerebral gray matter  24498 (20305, 28690)  21503 (17634, 25371)  19221 (16617, 21826)  Left cerebral gray matter  24229 (20255, 28203)  22401 (18735, 26067)  21056 (18588, 23525)  Right cerebral white matter  10778 (6842, 14715)  14824 (11192, 18456)  16782 (14336, 19227)  Left cerebral white matter  10910 (7323, 14498)  11939 (8629, 15248)  14621 (12392, 16849)  Cerebellum  9766 (8689, 10843)  9946 (8953, 10940)  9252 (8584, 9921)  Cerebellum, gray matter  6614 (4464, 8764)  7281 (5297, 9264)  5892 (4556, 7228)  Cerebellum, white matter  3152 (131, 6172)  2666 (−121, 5452)  3361 (1484, 5237)  Basal ganglia  4185 (3681, 4689)  4645 (4180, 5109)  4247 (3934, 4560)  Right basal ganglia  2075 (1784, 2367)  2305 (2036, 2574)  2130 (1949, 2311)  Left basal ganglia  2109 (1875, 2344)  2340 (2123, 2556)  2118 (1972, 2263)  Right caudate  650 (494, 807)  695 (550, 839)  690 (593, 787)  Left caudate  709 (596, 822)  643 (538, 747)  695 (624, 765)  Right lenticular nucleus  979 (889, 1069)  1012 (929, 1095)  990 (935, 1046)  Left lenticular nucleus  949 (867, 1031)  1067 (991, 1142)  970 (917, 1019)  Right amygdala  433 (272, 593)  574 (426, 722)  431 (331, 531)  Left amygdala  430 (274, 586)  594 (450, 738)  433 (336, 530)  Right acumbens  14 (−18, 45)  25 (−4, 53)  18 (−1, 38)  Left acumbens  21 (−20, 63)  36 (−2, 74)  22 (−4, 48)  Right hippocampus  530 (433, 627)  487 (398, 577)  479 (419, 539)  Left hippocampus  533 (432, 635)  502 (409, 595)  481 (418, 544)  Diencephalon  2913 (2583, 3243)  2368 (2063, 2672)  2695 (2490, 2901)  Right diencephalon  1364 (1198, 1531)  1162 (1009, 1316)  1354 (1251, 1458)  Left diencephalon  1549 (1346, 1752)  1206 (1018, 1393)  1341 (1215, 1467)  Right medial gray matter  1250 (1058, 1443)  1062 (884, 1240)  1244 (1124, 1364)  Left medial gray matter  1441 (1227, 1654)  1089 (892, 1286)  1243 (1110, 1375)  Right hypothalamus  114 (77, 151)  101 (67, 134)  110 (88, 133)  Left hypothalamus  108 (61, 155)  116 (73, 160)  98 (69, 127)  Brainstem  4838 (4473, 5202)  4740 (4404, 5077)  4906 (4680, 5133)  Midbrain  1828 (1604, 2052)  1812 (1605, 2019)  2039 (1900, 2178)  Pons  1937 (1648, 2226)  1771 (1504, 2037)  1750 (1571, 1930)  Medulla  1073 (821, 1324)  1158 (926, 1389)  1117 (960, 1273)  Ventricles  1288 (1060, 1515)  968 (758, 1178)  897 (756, 1039)  Right lateral  556 (455, 657)  430 (337, 523)  385 (322, 448)  Left lateral  564 (445, 682)  400 (291, 510)  380 (306, 454)  Third  42 (25, 59)  42 (26, 58)  34 (23, 44)  Cerebral aqueduct  30 (20, 39)  20 (12, 30)  23 (17, 29)  Fourth  97 (65, 128)  76 (47, 104)  76 (56, 95)  Corpus callosum  1514 (1233, 1795)  1638 (1378, 1897)  1348 (1174, 1523)  Septum  30 (14, 45)  31 (17, 45)  36 (26, 45)  Fornix  289 (192, 386)  278 (188, 367)  225 (164, 285)  Optic Chiasm  170 (118, 223)  158 (110, 206)  200 (167, 232)  Pineal  5 (−7, 16)  15 (5, 25)  8 (1, 15)  Pituitary  120 (75, 166)  155 (113, 197)  141 (113, 170)  Extra parenchymal volumea   41048 (11355, 70741)   42291 (41321, 43261)   42044 (36264, 47825)   Brain structure   Prenatal monkeys (n = 3)   Postnatal monkeys (n = 4)   Control monkeys (n = 8)   Total braina  98707 (64051, 133363)  90276 (78885, 101667)  98965 (91697, 106233)  Cerebrum  70415 (69126, 71705)  70666 (69476, 71856)  71680 (70879, 72481)  Right cerebrum  35276 (34327, 36225)  36326 (35451, 37202)  36003 (35414, 36593)  Left cerebrum  35139 (34039, 36240)  34340 (33325, 35355)  35677 (34994, 36361)  Right cerebral gray matter  24498 (20305, 28690)  21503 (17634, 25371)  19221 (16617, 21826)  Left cerebral gray matter  24229 (20255, 28203)  22401 (18735, 26067)  21056 (18588, 23525)  Right cerebral white matter  10778 (6842, 14715)  14824 (11192, 18456)  16782 (14336, 19227)  Left cerebral white matter  10910 (7323, 14498)  11939 (8629, 15248)  14621 (12392, 16849)  Cerebellum  9766 (8689, 10843)  9946 (8953, 10940)  9252 (8584, 9921)  Cerebellum, gray matter  6614 (4464, 8764)  7281 (5297, 9264)  5892 (4556, 7228)  Cerebellum, white matter  3152 (131, 6172)  2666 (−121, 5452)  3361 (1484, 5237)  Basal ganglia  4185 (3681, 4689)  4645 (4180, 5109)  4247 (3934, 4560)  Right basal ganglia  2075 (1784, 2367)  2305 (2036, 2574)  2130 (1949, 2311)  Left basal ganglia  2109 (1875, 2344)  2340 (2123, 2556)  2118 (1972, 2263)  Right caudate  650 (494, 807)  695 (550, 839)  690 (593, 787)  Left caudate  709 (596, 822)  643 (538, 747)  695 (624, 765)  Right lenticular nucleus  979 (889, 1069)  1012 (929, 1095)  990 (935, 1046)  Left lenticular nucleus  949 (867, 1031)  1067 (991, 1142)  970 (917, 1019)  Right amygdala  433 (272, 593)  574 (426, 722)  431 (331, 531)  Left amygdala  430 (274, 586)  594 (450, 738)  433 (336, 530)  Right acumbens  14 (−18, 45)  25 (−4, 53)  18 (−1, 38)  Left acumbens  21 (−20, 63)  36 (−2, 74)  22 (−4, 48)  Right hippocampus  530 (433, 627)  487 (398, 577)  479 (419, 539)  Left hippocampus  533 (432, 635)  502 (409, 595)  481 (418, 544)  Diencephalon  2913 (2583, 3243)  2368 (2063, 2672)  2695 (2490, 2901)  Right diencephalon  1364 (1198, 1531)  1162 (1009, 1316)  1354 (1251, 1458)  Left diencephalon  1549 (1346, 1752)  1206 (1018, 1393)  1341 (1215, 1467)  Right medial gray matter  1250 (1058, 1443)  1062 (884, 1240)  1244 (1124, 1364)  Left medial gray matter  1441 (1227, 1654)  1089 (892, 1286)  1243 (1110, 1375)  Right hypothalamus  114 (77, 151)  101 (67, 134)  110 (88, 133)  Left hypothalamus  108 (61, 155)  116 (73, 160)  98 (69, 127)  Brainstem  4838 (4473, 5202)  4740 (4404, 5077)  4906 (4680, 5133)  Midbrain  1828 (1604, 2052)  1812 (1605, 2019)  2039 (1900, 2178)  Pons  1937 (1648, 2226)  1771 (1504, 2037)  1750 (1571, 1930)  Medulla  1073 (821, 1324)  1158 (926, 1389)  1117 (960, 1273)  Ventricles  1288 (1060, 1515)  968 (758, 1178)  897 (756, 1039)  Right lateral  556 (455, 657)  430 (337, 523)  385 (322, 448)  Left lateral  564 (445, 682)  400 (291, 510)  380 (306, 454)  Third  42 (25, 59)  42 (26, 58)  34 (23, 44)  Cerebral aqueduct  30 (20, 39)  20 (12, 30)  23 (17, 29)  Fourth  97 (65, 128)  76 (47, 104)  76 (56, 95)  Corpus callosum  1514 (1233, 1795)  1638 (1378, 1897)  1348 (1174, 1523)  Septum  30 (14, 45)  31 (17, 45)  36 (26, 45)  Fornix  289 (192, 386)  278 (188, 367)  225 (164, 285)  Optic Chiasm  170 (118, 223)  158 (110, 206)  200 (167, 232)  Pineal  5 (−7, 16)  15 (5, 25)  8 (1, 15)  Pituitary  120 (75, 166)  155 (113, 197)  141 (113, 170)  Extra parenchymal volumea   41048 (11355, 70741)   42291 (41321, 43261)   42044 (36264, 47825)   Note. Structures that significantly (p < 0.05) differed among the three lead exposure groups by a 3(group) ANCOVA are in italics. Total brain volume was the covariate in these analyses. a Unadjusted volumes. View Large Dose response relationships were explored by correlating blood lead levels with the increased cerebral white matter and decreased lateral ventricle volumes. Partial correlations were calculated in order to adjust for total brain size. The partial correlations between blood lead levels in the study monkeys over the first six months of postnatal life were negatively but non-significantly correlated with white matter in the right (r = −0.34, p = 0.228) and the left (r = −0.38, p = 0.180) cerebral hemispheres. Those partial correlations were significant when maternal blood lead levels (during pregnancy for the prenatal monkeys and during nursing for the postnatal monkeys) rather than study monkey blood lead levels were correlated with cerebral white matter volumes (r = −0.62, p = 0.019 for the right cerebrum and r = −0.62, p = 0.018 for the left cerebrum). None of the partial correlations between blood levels and lateral ventricle volumes were significant although all four correlations were positive as expected. The changes in cerebral white matter volumes associated with measured blood lead levels were quantified by the slopes of linear regressions. There was a loss of 54.4 mm3 (95% C.I. = −38.9, 147.7 mm3) and 49.3 mm3 (95% C.I. = −26.1, 124.8 mm3) of right and left cerebral white matter respectively for every μg/dl increase in blood lead levels of the study monkeys. There were losses of 39.7 mm3 (95% C.I. = 7.8, 71.5 mm3) and 32.7 mm3 (95% C.I. = 6.7, 58.8 mm3) of right and left cerebral white matter respectively for every μg/dl increase in maternal blood lead levels. DISCUSSION Franklin et al. (2000) report that male whole brain volumes are approximately 20% larger than female whole brain volumes. As expected, lead exposed males had larger brains (by 16%) than lead exposed females in the study sample. However, male controls had similar sized brains as female controls. They were also similar in body weight suggesting by chance the sample included unusually small male control rhesus monkeys. Therefore, it would be misleading to interpret the gender × lead interaction in the study sample as a lead effect. Understanding gender differences in brain volumes is important. They must be addressed definitely with a larger and more representative sample. We did not detect reliable differences between lead exposed and control monkeys in total brain volume. Our ability to detect differences in total brain volume may have been compromised by unrepresentative sampling as discussed above. We evaluated lead effects on brain structures adjusting for total brain volume to reduce concerns about unrepresentative sampling. Monkeys exposed to lead early in life had less cerebral white matter in their brains than the control monkeys. The deficits were larger in monkeys exposed to lead prenatally. The blood lead levels of the mothers and the study monkeys were negatively correlated with cerebral white matter suggesting a dose response relationship. The relationships between blood lead levels and cerebral white matter may have been stronger with maternal blood lead levels than blood lead levels of the study monkeys because the latter only indirectly reflected lead exposure for the monkeys exposed prenatally. The exploratory nature of the analyses cautions against over-interpreting these results. A study by Deng and colleagues may explain why lead exposure was associated with a reduction in cerebral white matter in this study. Deng et al. studied the effects of lead exposure on rat oligodendrocyte progenitor cells and myelin production. Chronic lead exposure resulted in interference of the timely developmental maturation of oligodendrocyte progenitors resulting in hypo- and demyelination of axons (Deng et al., 2001). Because the overall size of the cerebrum was not affected by lead exposure in this study, cerebral tissue scored as gray matter on T1 MRIs may have been unmyelinated white matter. The differentiation of gray and white matter was done automatically on the basis of intensity level differences. Therefore, the trend to more cerebral gray matter in lead exposed monkeys may be explained by including unmyelinated white matter as “gray matter”. Alternatively, the fluid content in some cerebral tissue may be increased in lead exposed monkeys accounting for the reduced “white matter.” The increased size of the lateral ventricles also suggests that proportions of the tissues constituting the brain may be affected by exposure to lead early in development. The existing literature is too diverse at this point to confirm or refute these exploratory results. The few reports on the effects of early lead exposure on brain anatomy differ in lead exposures, species, measurement methodologies, and other factors. However, results of a previous study may explain in part why cerebral tissue may be differentially affected (Cremin et al., 1999). Adult rhesus monkeys were administered lead orally for five weeks to reach and maintain target blood lead levels of 35–40 μg/dl. Cremin et al. measured lead in the blood and the prefrontal cortex, frontal lobe, hippocampus, and striatum of the brain. Lead concentrations were greatest in the prefrontal cortex, followed by the frontal lobe, the hippocampus, and then the striatum. Although the Cremin et al. study concerned lead exposures as adults, a limited number of brain structures, and did not distinguish white and gray matter, it does suggest that lead may concentrate in the frontal cerebral cortex relative to the hippocampus and striatum. Thus, in the present study, cerebral white matter may have been differentially affected because of higher concentrations of lead in the cerebrum and because myelination is rapid during the period of lead exposure in this study. It is of note in the Cremin et al. study, that prefrontal cortex lead concentrations were significantly correlated with integrated blood lead levels over the entire lead exposure period but not with blood lead levels collected concurrently with the prefrontal cortex biopsy, emphasizing the necessity of animal models because lead exposure histories are not known in humans. In addition to reporting lead effects on the brain, our study employed a promising measurement methodology for neurotoxicological studies. MRIs are relatively non-invasive, increasing their potential applications. They avoid some of the distortions that are consequences of preserving (fixture effects) and evaluating the brain using laboratory methods. They are also efficient in that they can produce quantitative information concerning the brain with a relatively small investment in analysis time and effort. Non-invasive MRI and invasive laboratory approaches are complementary, MRIs providing a broader perspective that can be further refined by detailed laboratory analyses. Other MRI methodologies promise additional insights. For example, our results suggest myelination may be disrupted in monkeys exposed to lead early in life. Diffusion Tensor Imaging (DTI) is a more direct and sensitive approach to evaluating myelination. Follow-up studies to identify brain insults due to early lead exposure should include DTI. In addition, MRS has been used to identify neuronal loss in the forebrains of lead exposed children and is another imaging methodology to consider in lead research (Trope et al., 1998, 2001). Finally, functional MRI directly relates functional deficits to specific brain structures significantly enhancing our understanding of the effects of lead on the brain. An advantage of all these approaches is they can be conducted efficiently and non-invasively in humans and laboratory animals facilitating cross-species comparisons. In short, MRI technologies have much to offer research evaluating the effects of lead and other environmental toxins on the brains of humans and laboratory animals. The scoring procedures adopted for this study were demonstrated to be highly reliable. There are several factors that contributed to the consistency of scoring. Some of the procedures were semi-automated identifying all voxels within a defined intensity range as belonging to the defined structures. It is not surprising that semi-automated approaches to identifying structures are highly reliable. It was surprising that very high reliabilities were achieved for structures defined manually. The reliability of scoring the MRI structures benefited from the way volumetric data are scored and generated. Minor differences between independent measurements of the same brain are likely to be randomly distributed. By summing measurements from successive slices to calculate volumetric measurements, those random measurement errors tend to cancel each other resulting in highly reliable measurements. For an individual slice, the reliabilities are more modest than the reliabilities presented in Table 3. It should be noted that only intra-scorer reliability was assessed in this study. The measurement errors associated with different scorers (inter-scorer reliability) and repeating an MRI on the same subject (test-retest reliability) were not assessed. The former is critical in estimating the generalizability of the results by other investigators, the latter in estimating how well an individual MRI characterizes the brain of the monkey. A complete characterization of the measurement errors associated with quantitative MRI methods requires evaluating all sources of measurement variability. Nevertheless, our reliability results indicate that the differences reported for the MRIs on the study sample are robust. Whether we would replicate our results with a different scorer or if we obtained different scans on the study monkeys are important questions we did not address in this study. For this study, we chose to employ manual and semi-automated methods to analyze total and component brain volumes. Fully automated methods for segmentation offer greater objectivity, replicability, generalizability, and efficiency (Ashburner and Friston, 2000; Caviness et al., 1999; Fischl et al., 2002). Furthermore, they presuppose no working knowledge of brain anatomy. At present we believe that semi-automated methods involving a scorer with knowledge of rhesus brain anatomy will produce more accurate results for some structures. Statistical pattern recognition methods based on a finite mixture model that partition the brain into gray matter, white matter, and CSF have been reported by Anderson et al. (2002). Automated methods are beginning to incorporate anatomical landmarks into the identification of brain structures because relying on gray scale differences alone is inadequate to distinguish all brain structures. Multispectral approaches that combine the information from different scanning methodologies (e.g., T1 and T2) to make discriminations that cannot be differentiated by the individual scanning methodologies afford improved identification capabilities. Because of the advantages of automated methods, conducting and interpreting studies of the brain can be greatly facilitated. There have also been considerable gains in the analysis and interpretation of MRI scans. Notable is the work of Friston, Worsley, Ashburner, and colleagues. They have developed Statistical Parametric Mapping (SPM) to test hypotheses about imaging data, primarily fMRI and PET. Their approach is also suitable for segmentation studies and includes Voxel-Based Morphometry or VBM (Ashburner and Friston, 2000). Their analytic software is available at no cost to the neuro-imaging community (www.fil.ion.ucl.ac.uk/spm/). Their analytic approach evaluates the relationships between design effects (experimental groups or stimulus effects) and voxel intensity adjusting for identified covariates using a general linear model (GLM) framework. The results of their analyses are statistical maps that identify the likelihood of group (or stimulus in the case of functional imaging) differences at an individual voxel level. They adjust for multiple comparisons using Gaussian Random Field (GRF) theory. GRF rather than a Bonferroni type correction is used because voxel intensities are not independent but are correlated. Their approach lends itself to hierarchical mixed modeling in which voxels could be identified as belonging to higher level factors such as brain structures and those to even higher levels factors such as experimental groups. Prior information can be incorporated from previous scans, other measurement modalities, or from the literature by adopting a Bayesian framework. Despite the appeal of Friston et al.'s approach, we did not adopt it for this study. Their approach is voxel based; ours is anatomical structure based. The distinctions they make depend on intensity level differences. For the reasons we have alluded to, we have felt it necessary to also include anatomical landmarks to identify many brain structures of interest. An SPM identifies voxels that are likely to differ in intensity between lead exposed and control monkeys. To do so the images must be co-registered so that “lead exposed monkey” voxels can be compared to the corresponding “control monkey” voxels. Co-registering is not a trivial task and best achieved when the referent is well-characterized (artifact free and representative of the referent population) and the comparison group is not markedly discrepant from the referent. The more discrepant the two groups the more smoothing of the voxels required for co-registration, compromising the resolution of the analysis. Small sample sizes such as the present study exacerbate these requirements for valid co-registration. In contrast, the approach we adopted does not require co-registering of the scans but does require the identification of the structures of interest by a trained scorer. By using hierarchical mixed modeling or planned rather post hoc comparisons, it would be possible to define “anatomical contrasts” within a VBM approach similar to those we have made; however, it would require a priori identification of the voxels belonging to those anatomical structures and, therefore, the same anatomical classification our approach depends on. Our approach also differs from Friston's VBM approach in that we did not adjust our results for multiple comparisons. Specifically, our concern was not false positives but missing true differences if they exist. Because of the sheer number of voxels analyzed, adjusting for dependences among those voxels is necessary for VBM. The number of anatomical structures we analyzed, although sizable, was more manageable. Furthermore, all structures we identified were not equal (although we did not feel planned comparisons were appropriate given a sparse literature). Specifically, we identified differences in cerebral white matter. Cerebral white matter was automatically identified, reliably scored, and among the largest structures scored. We measured many structures because the specifics of lead effects on the brain are largely unknown. Therefore, our results may be explained by chance. Furthermore, we could only expect to identify lead effects that were sizeable because of our small sample size. Therefore, our study is vulnerable to missing more subtle lead effects. We did demonstrate that highly reliable volumetric measurements of brain structures can be recorded from adult rhesus monkey MRIs. Consequently, the differences we reported characterize the study monkeys on the MRIs collected for this study. However, it is less clear whether these results would generalize to other samples of monkeys (or humans) or even to the same monkeys with different MRIs. This study needs replication on a larger sample. Nevertheless, our results suggest that early lead exposure may disrupt cerebral myelination in rhesus monkeys. The authors would like to thank Nicole Giles, Heather Gottfried, and Kyle Meyer for their technical assistance with this project. The data for this study were collected under a grant from NIEHS awarded to N.K.L. (NIH R01 ES04860). The analyses were supported by a grant awarded to R.E.L. from the Center for Clinical Research and Evidence-Based Medicine, the University of Texas-Houston Medical School. References Anderson, A. H., Zhang, Z., Avison, M. J., and Gash, D. M. ( 2002). Automated segmentation of multispectral brain MR images. J. Neurosci. Methods  122, 13–23. Google Scholar Ashburner, J., and Friston, K. J. ( 2000). Voxel-based morphometry-the methods. Neuroimage  11, 805–821. Google Scholar Bellinger, D. C., and Needleman, H. L. ( 2003). Intellectual impairment and blood lead levels. N. Engl. J. Med.  349, 500–502. Google Scholar Bland, J. M., and Altman, D. G. ( 1996). Statistics notes: Measurement error and correlation coefficients. BMJ  313, 41–42. Google Scholar BrainInfo. View the Template Atlas page. Available at: http://braininfo.rprc.washington.edu. Accessed 10 February 2004 and 30 June 2004. Google Scholar Canfield, R. L., Henderson, C. R., Jr., Cory-Slechta, D. A., Cox, C., Jusko, T. A., and Lanphear, B. P. ( 2003). Intellectual impairment in children with blood lead concentrations below 10 microg per deciliter. N. Engl. J. Med.  348, 1517–1526. Google Scholar Caviness, V. S., Jr., Lange, N. T., Makris, N., Herbert, M. R., and Kennedy, D. N. ( 1999). MRI-based brain volumetrics: Emergence of a developmental brain science. Brain Dev.  21, 289–295. Google Scholar Comparative Mammalian Brain Collections. The Rhesus Monkey page. Available at: www.brainmuseum.org. Accessed 10 February 2004 and 30 June 2004. Google Scholar Cremin, J. D., Jr., Luck, M. L., Laughlin, N. K., and Smith, D. R. ( 1999). Efficacy of succimer chelation for reducing brain lead in a primate model of human lead exposure. Toxicol. Appl. Pharmacol.  161, 283–293. Google Scholar Cupp, C. J., and Uemura, E. ( 1981). Body and organ weights in relation to age and sex in Macaca mulatta. J. Med. Primatol.  10, 110–123. Google Scholar Deng, W., McKinnon, K. D., and Poretz, R. D. ( 2001). Lead exposure delays the differentiation of oligodendroglial progenitors in vitro. Toxicol. Appl. Pharmacol.  174, 235–244. Google Scholar Ediger, R. D., and Coleman, R. L. ( 1972). A modified Delves cup atomic absorption procedure for the determination of lead in blood. At. Absopt. Newsl.  11, 33–36. Google Scholar Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., Montillo, A., Makris, N., Rosen, B., and Dale, A. M. ( 2002). Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron  33, 341–355. Google Scholar Franklin, M. S., Kraemer, G. W., Shelton, S. E., Baker, E., Kalin, N. H., and Uno, H. ( 2000). Gender differences in brain volume and size of corpus callosum and amygdala of rhesus monkey measured from MRI images. Brain Res.  852, 263–267. Google Scholar Institute of Laboratory Animal Resources, Committee on the Care and Use of Laboratory Animals. ( 1985). Guide for the care and use of laboratory animals. NIH publication #86-23. U.S. Department of Health and Human Services, Washington, DC. Google Scholar Iosifescu, D. V., Shenton, M. E., Warfield, S. K., Kikinis, R., Dengler, J., Jolesz, F. A., and McCarley, R. W. ( 1997). An automated registration algorithm for measuring MRI subcortical brain structures. Neuroimage  6, 13–25. Google Scholar Lasky, R. E., Maier, M., Snodgrass, E., Hecox, K. E., and Laughlin, N. K. ( 1995). The effects of lead on otoacoustic emissions and auditory evoked potentials in monkeys. Neurotoxicol. Teratol.  17, 633–644. Google Scholar Laboratory of Neuroimaging, UCLA. The Monkey Atlas page. Available at: www.loni.ucla.edu/Research/Atlases/MonkeyAtlas.html. Accessed 10 February 2004 and 30 June 2004. Google Scholar Ma, T., Chen, H. H., Lim, D. K., Hume, A. S., and Ho, I. K. ( 1997). Effects of subacute lead exposure on [3H]MK-801 binding in hippocampus and cerebral cortex in the adult rat. Brain Res.  760, 187–192. Google Scholar Mani, J., Chaudhary, N., Kanjalkar, M., and Shah, P. U. ( 1998). Cerebellar ataxia due to lead encephalopathy in an adult. J. Neurol. Neurosurg. Psychiatry  65, 797. Google Scholar Martin, R. F., and Bowden, D. M. ( 1996). A stereotaxic template atlas of the macaque brain for digital imaging and quantitative neuroanatomy. Neuroimage  4, 119–150. Google Scholar Matochik, J. A., Chefer, S. I., Lane, M. A., Woolf, R. I., Morris, E. D., Ingram, D. K., Roth, G. S., and London, E. D. ( 2000). Age-related decline in striatal volume in monkeys as measured by magnetic resonance imaging. Neurobiol. Aging  21, 591–598. Google Scholar Needleman, H. L., and Landrigan, P. J. ( 2004). What level of lead in blood is toxic for a child? Am. J. Public Health  94, 8. Google Scholar Paxinos, G., Huang, X. F., and Toga, A. W. ( 2000). The Rhesus Monkey Brain in Stereotaxic Coordinates. Academic Press, San Diego, CA. Google Scholar Reddy, G. R., Basha, M. R., Devi, C. B., Suresh, A., Baker, J. L., Shafeek, A., Heinz, J., and Chetty, C. S. ( 2003). Lead induced effects on acetylcholinesterase activity in cerebellum and hippocampus of developing rat. Int. J. Dev. Neurosci.  21, 347–352. Google Scholar Reuhl, K. R., Rice, D. C., Gilbert, S. G., and Mallett, J. ( 1989). Effects of chronic developmental lead exposure on monkey neuroanatomy: Visual system. Toxicol. Appl. Pharmacol.  99, 501–509. Google Scholar Rothman, K. J. ( 1990). No adjustments are needed for multiple comparisons. Epidemiology  1, 43–46. Google Scholar Schantz, S. L., Laughlin, N. K., Van Valkenberg, H. C., and Bowman, R. E. ( 1986). Maternal care by rhesus monkeys of infant monkeys exposed to either lead or 2,3,7,8-tetrachlorodibenzo-p-dioxin. Neurotoxicology  7, 637–650. Google Scholar Schindler, M. K., Wang, L., Selemon, L. D., Goldman-Rakic, P. S., Rakic, P., and Csernansky, J. G. ( 2002). Abnormalities of thalamic volume and shape detected in fetally irradiated rhesus monkeys with high dimensional brain mapping. Biol. Psychiatry  51, 827–837. Google Scholar Schroter, C., Schroter, H., and Huffmann, G. ( 1991). Neurologic and psychiatric manifestations of lead poisoning in adults (case report and review of the literature). Fortschr. Neurol. Psychiatr.  59, 413–424. Google Scholar Slomianka, L., Rungby, J., West, M. J., Danscher, G., and Andersen, A. H. ( 1989). Dose-dependent bimodal effect of low-level lead exposure on the developing hippocampal region of the rat: A volumetric study. Neurotoxicology  10, 177–190. Google Scholar Smith, S. M., Zhang, Y., Jenkinson, M., Chen, J., Matthews, P. M., Federico, A., and De Stefano, N. ( 2002). Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage  17, 479–489. Google Scholar Stephenson, J. ( 2003). CDC report on environmental toxins: Some progress, some concerns. JAMA  289, 1230–1233. Google Scholar Struzynska, L., Bubko, I., Walski, M., and Rafalowska, U. ( 2001). Astroglial reaction during the early phase of acute lead toxicity in the adult rat brain. Toxicology  165, 121–131. Google Scholar Teo, J. G., Goh, K. Y., Ahuja, A., Ng, H. K., and Poon, W. S. ( 1997). Intracranial vascular calcifications, glioblastoma multiforme, and lead poisoning. AJNR Am. J. Neuroradiol.  18, 576–579. Google Scholar Trope, I., Lopez-Villegas, D., Cecil, K. M., and Lenkinski, R. E. ( 2001). Exposure to lead appears to selectively alter metabolism of cortical gray matter. Pediatrics  107, 1437–1442. Google Scholar Trope, I., Lopez-Villegas, D., and Lenkinski, R. E. ( 1998). Magnetic resonance imaging and spectroscopy of regional brain structure in a 10-year-old boy with elevated blood lead levels. Pediatrics  101, E7. Google Scholar Tuzun, M., Tuzun, D., Salan, A., and Hekimoglu, B. ( 2002). Lead encephalopathy: CT and MR findings. J. Comput. Assist. Tomogr.  26, 479–481. Google Scholar Wei, X., Warfield, S. K., Zou, K. H., Wu, Y., Li, X., Guimond, A., Mugler, J. P. 3rd, Benson, R. R., Wolfson, L., Weiner, H. L., and Guttmann, C. R. ( 2002). Quantitative analysis of MRI signal abnormalities of brain white matter with high reproducibility and accuracy. J. Magn. Reson. Imaging  15, 203–209. Google Scholar Weisskopf, M. G., Hu, H., Mulkern, R. V., White, R., Aro, A., Oliveira, S., and Wright, R. O. ( 2004). Cognitive deficits and magnetic resonance spectroscopy in adult monozygotic twins with lead poisoning. Environ. Health Perspect.  112, 620–625. Google Scholar Author notes *Center for Clinical Research and Evidence Based Medicine, The University of Texas-Houston Medical School, 6431 Fannin Street, MSB 2.104, Houston, Texas 77030; †Harlow Center for Biological Psychology, 22 North Charter Street, Madison, Wisconsin 53715; ‡The University of Texas-Houston Medical School, Department of Pediatrics, 6431 Fannin, Houston, Texas 77030 Toxicological Sciences vol. 85 no. 2 © The Author 2005. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
TI - The Effects of Early Lead Exposure on the Brains of Adult Rhesus Monkeys: A Volumetric MRI Study
JF - Toxicological Sciences
DO - 10.1093/toxsci/kfi153
DA - 2005-03-23
UR - https://www.deepdyve.com/lp/oxford-university-press/the-effects-of-early-lead-exposure-on-the-brains-of-adult-rhesus-5D8uUuuPcT
SP - 963
EP - 975
VL - 85
IS - 2
DP - DeepDyve
ER -