prenatal ethanol exposure, generalized learning impairment, and medial prefrontal cortical deficits...

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Prenatal ethanol exposure, generalized learning impairment, and medial prefrontal cortical deficits in rats Sheila M. Mihalick*, James E. Crandall, Jason C. Langlois, Jason D. Krienke, William V. Dube 1 Psychological Sciences Division, University of Massachusetts Medical School-Shriver Center, 200 Trapelo Road, Waltham, MA 02452-6319, USA Received 16 October 2000; received in revised form 19 April 2001; accepted 12 July 2001 Abstract Prenatal ethanol exposure may cause neurological damage and subsequent mental retardation in humans, with learning deficits similar to those following damage to the prefrontal cortex. This study examined cognitive dysfunction and cortical damage after prenatal exposure to ethanol using a chronic administration model. Pregnant Sprague – Dawley rats received one of three diets during gestation: a liquid diet containing 35% ethanol-derived calories (ETOH), an isocaloric liquid diet (ISO), or standard chow (CHOW). Subjects were obtained from ETOH dams with blood alcohol concentrations (BACs) above 90 mg/dl and corresponding ISO and CHOW controls (one male pup/litter; n = 6 pups/group). At approximately 90 days of age, subjects began training on a series of unique auditory discrimination problems using a successive go/no-go procedure. A criterion of 85% accuracy determined when a rat continued to the next problem. Subjects completed a varying number of problems within a 30-session limit, after which all rats were tested on a tone/click discrimination and reversal. Subjects were then sacrificed and neuronal number in the medial prefrontal cortex (mPFC) was estimated by the optical fractionator method. Prenatal ethanol exposure induced significant cell loss in the mPFC, which was associated with significantly impaired reversal learning. Poor performance by ETOH subjects on the tone/click reversal indicates a transfer of training deficit that may reflect failures of inhibitory control. D 2001 Elsevier Science Inc. All rights reserved. Keywords: FAS; Prenatal ethanol; Reversal learning; Rats; Transfer of training; Auditory discrimination; Prefrontal cortex 1. Introduction Prenatal exposure to alcohol may result in Fetal Alcohol Syndrome (FAS), with symptoms including craniofacial dysmorphism and central nervous system damage (reviewed in Ref. [75]). The behavioral profile of FAS in humans includes developmental delays [3,4,44], problems with inhibitory control [48], and poor performance on tests of intelligence, working memory, and academic achievement (e.g., Ref. [14,75,77]). FAS is one of the leading known causes of human mental retardation [2]. The neurological damage primarily associated with FAS includes microencephaly with disorganization of the cereb- ral cortex, cerebellum, and basal ganglia as indicated by neuroglial heterotopias and major white matter tract dys- genesis (reviewed in Ref. [13]). It is not known exactly how alcohol disrupts neurodevelopment, but it could impair any or all of the following processes: cell proliferation and death, neuronal migration, neurite outgrowth, synaptogene- sis, and myelination (reviewed in Refs. [33,86]). The few human FAS brains that have been analyzed show neuro- pathologic features indicating disruption of cytogenesis and/ or neuronal migration [29,68]. Efforts to model FAS in rodents have generated much data from many different paradigms, creating an extensive but diffuse picture of prenatal ethanol effects in the rat. In general, the data obtained from rodent FAS models correspond well to the human profile. Rats exposed prenatally to ethanol typ- ically show developmental delays (e.g., Ref. [42]), inhibitory deficits (e.g., Ref. [64]), and impairments on tests of spatial ability (e.g., Ref. [28]), working memory (e.g., Ref. [61]), and reversal learning (e.g., Ref. [45]). The neuropathologic fea- tures observed in rodent FAS models include disrupted neuronal and glial development in the cerebellum, hippocam- pus, and olfactory bulb (e.g., Refs. [9,23,26,88]). 0892-0362/01/$ – see front matter D 2001 Elsevier Science Inc. All rights reserved. PII:S0892-0362(01)00168-4 * Corresponding author. Tel.: +1-607-255-1839. E-mail addresses: [email protected] (S.M. Mihalick), [email protected] (W.V. Dube). 1 Also corresponding author. Neurotoxicology and Teratology 23 (2001) 453– 462

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Prenatal ethanol exposure, generalized learning impairment, and medial

prefrontal cortical deficits in rats

Sheila M. Mihalick*, James E. Crandall, Jason C. Langlois,Jason D. Krienke, William V. Dube1

Psychological Sciences Division, University of Massachusetts Medical School-Shriver Center, 200 Trapelo Road, Waltham, MA 02452-6319, USA

Received 16 October 2000; received in revised form 19 April 2001; accepted 12 July 2001

Abstract

Prenatal ethanol exposure may cause neurological damage and subsequent mental retardation in humans, with learning deficits similar to

those following damage to the prefrontal cortex. This study examined cognitive dysfunction and cortical damage after prenatal exposure to

ethanol using a chronic administration model. Pregnant Sprague–Dawley rats received one of three diets during gestation: a liquid diet

containing 35% ethanol-derived calories (ETOH), an isocaloric liquid diet (ISO), or standard chow (CHOW). Subjects were obtained from

ETOH dams with blood alcohol concentrations (BACs) above 90 mg/dl and corresponding ISO and CHOW controls (one male pup/litter;

n = 6 pups/group). At approximately 90 days of age, subjects began training on a series of unique auditory discrimination problems using a

successive go/no-go procedure. A criterion of 85% accuracy determined when a rat continued to the next problem. Subjects completed a

varying number of problems within a 30-session limit, after which all rats were tested on a tone/click discrimination and reversal. Subjects

were then sacrificed and neuronal number in the medial prefrontal cortex (mPFC) was estimated by the optical fractionator method. Prenatal

ethanol exposure induced significant cell loss in the mPFC, which was associated with significantly impaired reversal learning. Poor

performance by ETOH subjects on the tone/click reversal indicates a transfer of training deficit that may reflect failures of inhibitory control.

D 2001 Elsevier Science Inc. All rights reserved.

Keywords: FAS; Prenatal ethanol; Reversal learning; Rats; Transfer of training; Auditory discrimination; Prefrontal cortex

1. Introduction

Prenatal exposure to alcohol may result in Fetal Alcohol

Syndrome (FAS), with symptoms including craniofacial

dysmorphism and central nervous system damage (reviewed

in Ref. [75]). The behavioral profile of FAS in humans

includes developmental delays [3,4,44], problems with

inhibitory control [48], and poor performance on tests of

intelligence, working memory, and academic achievement

(e.g., Ref. [14,75,77]). FAS is one of the leading known

causes of human mental retardation [2].

The neurological damage primarily associated with FAS

includes microencephaly with disorganization of the cereb-

ral cortex, cerebellum, and basal ganglia as indicated by

neuroglial heterotopias and major white matter tract dys-

genesis (reviewed in Ref. [13]). It is not known exactly how

alcohol disrupts neurodevelopment, but it could impair any

or all of the following processes: cell proliferation and

death, neuronal migration, neurite outgrowth, synaptogene-

sis, and myelination (reviewed in Refs. [33,86]). The few

human FAS brains that have been analyzed show neuro-

pathologic features indicating disruption of cytogenesis and/

or neuronal migration [29,68].

Efforts to model FAS in rodents have generated much data

from many different paradigms, creating an extensive but

diffuse picture of prenatal ethanol effects in the rat. In general,

the data obtained from rodent FASmodels correspond well to

the human profile. Rats exposed prenatally to ethanol typ-

ically show developmental delays (e.g., Ref. [42]), inhibitory

deficits (e.g., Ref. [64]), and impairments on tests of spatial

ability (e.g., Ref. [28]), workingmemory (e.g., Ref. [61]), and

reversal learning (e.g., Ref. [45]). The neuropathologic fea-

tures observed in rodent FAS models include disrupted

neuronal and glial development in the cerebellum, hippocam-

pus, and olfactory bulb (e.g., Refs. [9,23,26,88]).

0892-0362/01/$ – see front matter D 2001 Elsevier Science Inc. All rights reserved.

PII: S0892 -0362 (01 )00168 -4

* Corresponding author. Tel.: +1-607-255-1839.

E-mail addresses: [email protected] (S.M. Mihalick),

[email protected] (W.V. Dube).1 Also corresponding author.

Neurotoxicology and Teratology 23 (2001) 453–462

Several lines of evidence suggest the potential value of

research on ethanol-related damage to the medial prefrontal

cortex (mPFC). First, continuing studies by Miller

[51,52,54], Miller and Kuhn [55], Miller and Potempa

[56], and Miller and Robertson [57] indicate that prenatal

ethanol alters many aspects of typical cortical development

by interfering with cell cycle kinetics, neuronal migration,

glial genesis and transformation, cortical connectivity, and

cell number [24,25,27,32,59]. Second, the cortex is vulner-

able to ethanol toxicity. Ethanol decreases cortical thickness

[42], kills cortical cells in vitro [73], and alters the in vivo

expression of proteins involved in cortical neuron death [43].

Finally, the functional deficits that are observed after

damage to mPFC resemble those seen in FAS and/or mental

retardation. Prefrontal damage is consistent with deficits in

generalized learning, strategic and organizational functions,

and flexibility to changing environmental demands (e.g.,

Ref. [41]). For example, Winocur and Moscovitch [93]

demonstrated that rats with lesions of mPFC were impaired

on transfer of training from one version of a maze to

another, but not on learning that depended on maze-specific

information. As another example, in an animal model of

experimental phenylketonuria in rats, Strupp et al. [78]

found no deficiency in learning during initial training with

different types of problems. There was, however, a defi-

ciency in the transfer of learning that was detected only

when the animals were given new exemplars of each type of

problem. The potential relevance of this finding to mPFC

comes from studies by Diamond et al. [15] linking experi-

mental PKU in rats to neurotransmitter abnormalities spe-

cifically in the prefrontal cortex. Strupp et al. noted that their

finding corresponds with the human literature. Humans with

mental retardation may not be impaired relative to typically

developing individuals on simple tests with salient stimuli,

but they are more likely to exhibit impairments on more

complex tests that require transfer of training [10,11,39,50].

Previous studies with rat models of prenatal ethanol

exposure have not specifically examined the possible asso-

ciation between cortical damage and cognitive deficits.

Although some works on prenatal ethanol do relate behav-

ioral deficits and altered gene expression in PFC [60], there

have been no attempts to link learning impairments and

cortical injury per se. The present experiment was designed

to determine whether prenatal ethanol adversely affects cell

number in rat mPFC and whether exposure leads to cogni-

tive deficits consistent with mPFC damage.

This study used a prenatal chronic exposure model and

examined transfer of training in ethanol-exposed rats, iso-

caloric controls, and ad libitum chow-fed controls. The

initial training was a series of two-choice discrimination

problems with a different pair of auditory stimuli for each

problem. The transfer test was a discrimination reversal

problem in which the positive and negative stimulus func-

tions for a specific pair of stimuli were reversed. Positive

transfer has been reported in several species between these

two tasks (e.g., Refs. [35,37,38,69–72,83]). The major

dependent measures were performance on initial discri-

mination learning, on the transfer test, and neuronal number

in mPFC as estimated by the optical fractionator stereo-

logical method [90].

2. Methods

Mature nulliparous Sprague–Dawley albino females

obtained from Charles River Laboratories (Wilmington,

MA, USA) were timed-mated with mature males. Upon

detection of a vaginal plug (gestation day, or GD, 1),

females were isolated and randomly assigned to the diet

administration groups described below. Dams were housed

in individual cages, with ad libitum access to water, on a

12-h light/dark cycle with lights on at 7 a.m.

2.1. Diet administration

Animals in ethanol and isocaloric groups (described

below) were fed Bio-Serv high-protein liquid diet (Bio-Serv

1265SP; Bio-Serv, Frenchtown, NJ, USA). The diet was

presented in graduated feeding tubes (Bio-Serv 9010) over-

night from 6 p.m. to 9 a.m. Standard lab chow was

unavailable for these groups from GD2 through GD20. On

GD1, all dams assigned to receive the ethanol or isocaloric

diets were fed 30 ml of the liquid control diet in addition to

ad libitum chow. This procedure was done to help minimize

the potential effects of food neophobia (e.g., Ref. [30]).

From GD2 to GD20, dams received the appropriate liquid

diet (ethanol or isocaloric control) only. On GD21, liquid

diet was discontinued and ad libitum chow reinstated.

2.1.1. Ethanol exposure

Nine dams in the ethanol diet group (ETOH) were given

a 100-ml mixture of one-third ethanol diet and two-thirds

isocaloric diet (12% ethanol-derived calories, or EDC, and

23% maltose dextrin-derived calories, or MDC) on GD2–3;

two-thirds ethanol diet and one-third isocaloric diet (23%

EDC and 12% MDC) on GD4–6; and ethanol diet only

(35% EDC) on GD7–20.

2.1.2. Isocaloric control

Each of the nine dams in the isocaloric control group

(ISO) was yoked to an ETOH dam and pair-fed the

isocaloric control diet (35% MDC) on a milliliter per

kilogram weight basis.

2.1.3. Standard diet control

All nine dams in the standard diet control group

(CHOW) had ad libitum access to standard chow.

2.2. Blood alcohol concentration (BAC)

On GD16, 50 ml of blood was collected from the tails of

ETOH dams between 7:30 and 8:00 a.m. (e.g., Ref. [47];

S.M. Mihalick et al. / Neurotoxicology and Teratology 23 (2001) 453–462454

previous research indicates that the highest rate of consump-

tion for liquid diet presented at 7:00 p.m. is between 3:00

and 7:00 a.m. [91]). Tails of ISO and CHOW dams were

also nicked, but no blood was collected. The GD chosen to

measure BAC was consistent with previous work [1,31,47,

53,62,66,92].

BAC was determined by an enzymatic spectrophotomet-

ric assay for alcohol concentration (Sigma Diagnostics kit

no. 332; Sigma, St. Louis, MO). Samples were read at

340 nm UV using a Beckman DU 650 spectrophotometer.

BAC ranged from 16.3 to 159.20 mg/dl.

Three ETOH dams with BAC less than 90 mg/dl were

eliminated from the experiment along with their correspond-

ing pair-fed ISO dams. Individual differences in pattern of

intake may affect peak blood alcohol level [65]. Previous

studies of behavioral or cognitive deficits following prenatal

ethanol administration via 35% EDC liquid diet typically

report BAC levels of 100–120 mg/dl [1,47,92] (also see

Ref. [30]). The 90 mg/dl cutoff point was chosen for

consistency with previous research.

The six remaining ETOH dams provided experimental

subjects; their mean ethanol consumption for GD7–20 was

11.48 g/kg/day, and mean BAC was 116.98 mg/dl.

2.3. Subjects

Experimental subjects consisted of one male pup selected

at random from each of the six ETOH, pair-fed ISO, and

CHOW litters (n= 6 pups/group). One pup per litter was

chosen to avoid artificially constrained variance due to litter

effects. Females were not used as subjects to avoid potential

confounds arising from fluctuations in learning that may

correspond to estrus-related changes in brain plasticity

[84,85].

On postnatal day (PD) 1, the pups were placed with

lactating, chow-fed foster mothers. Thus, litters were not

preserved and pups were distributed among foster dams so

that each new litter consisted of two ETOH, two ISO, two

CHOW, and three foster pups. Subjects were weaned on

PD21 and housed two per cage.

Subjects were isolated at PD85 and reduced to 80–85%

of their free-feeding weights by food restriction. Weights

were monitored daily and maintained at 85 ± 5% of free-

feeding weight by supplemental feedings. Water was always

available in the home cages. Behavioral testing commenced

at approximately PD90. Each animal’s session was run

during the light phase at approximately the same time each

day, 6 days/week.

2.4. Apparatus and stimuli

Each subject was tested in its home cage (46�26�21 cm), which was placed inside a sound-attenuating

enclosure before each session. The testing apparatus was

mounted on a plastic panel that replaced the cage top. Two

stainless steel columns (14� 6� 3 cm) were suspended

from the panel into opposite ends of the cage. The column

at one end was fitted with a response lever (Lever 1), an

audio speaker (Panasonic Micro Speaker EAS-45P36S;

Panasonic, Kent, WA, USA) behind a steel screen, and a

steel cup into which 45-mg food pellets (Bio-Serv F0165)

were dispensed. The other column was fitted only with a

response lever (Lever 2). Macintosh computers presented

and recorded all session events (for details, see Ref. [18]).

Auditory stimuli for discrimination problems were 12

digitized sound effects, sampled music, and synthesized

complex wave forms (details available from the authors).

Continuously repeating segments of 0.4 s duration were

presented via the Macintosh external speaker circuit at 72 dB

SPL measured 2 cm from the response lever.

2.5. Preliminary training

Preliminary training is outlined very briefly in this

section; specific details are available from the authors.

Pretraining consisted of three phases. In Phase 1, animals

habituated to the apparatus and pellet dispenser. In Phase 2,

animals were trained to press the lever adjacent to the pellet

magazine (Lever 1) to produce pellets. In Phase 3, animals

were trained to perform a two-press sequence: Pressing

Lever 2 produced continuously repeating cycles of an

auditory stimulus (different from those used in the sub-

sequent experiment), then pressing Lever 1 terminated the

stimulus and produced a pellet. After animals completed 60

two-lever sequences, they advanced to the successive dis-

crimination condition.

2.6. Successive discrimination condition

Using a successive go/no-go procedure, subjects were

tested on a series of auditory discrimination problems. Each

problem consisted of a novel pair of stimuli, and none of the

stimulus pairings was repeated within groups. Across

groups, the specific stimuli assigned to each ETOH subject

were also assigned to its corresponding ISO subject and one

CHOW subject.

Subjects produced auditory stimuli by pressing Lever 2.

On positive (go) trials, the auditory stimulus (S+) was

presented continuously until the subject pressed Lever 1

and a pellet was dispensed. A correct response was defined

as pressing Lever 1 within 5 s of the S+ stimulus onset.

Presses that occurred after 5 s were reinforced, but not scored

as correct responses. On negative (no-go) trials, the stimulus

(S� ) was presented continuously for 5 s and then termi-

nated without a reinforcer. Lever 1 presses had no pro-

grammed consequences. A correct response was recorded

if Lever 1 was not pressed during the S� presentation (for

further details, see Ref. [17]).

Sessions consisted of 120 trials, 60 S+ and 60 S�, presen-

ted randomly with the restriction that the same stimulus was

presented no more than three times consecutively. The

learning criterion for this and all subsequent phases of testing

S.M. Mihalick et al. / Neurotoxicology and Teratology 23 (2001) 453–462 455

was at least 85% correct responses for two consecutive

sessions. After each subject met this criterion, it was given

a new problem. The successive discrimination condition

continued in this manner for 30 sessions.

2.7. Discrimination reversal condition

After completing the successive discrimination con-

dition, all subjects were presented with a novel discrimina-

tion problem. The stimuli for this problem were a 2-kHz

tone and a 10-Hz click (tone/click), with S+/S� assign-

ments counterbalanced within groups. After subjects met the

learning criterion for this problem, the reinforcement con-

tingencies for both stimuli were reversed in the next session.

Thus, the previous S+ became the S� , and vice versa. The

reversal condition continued for a maximum of 30 sessions.

Subjects were eliminated if they failed to reach at least one

criterion (i.e., if they failed to learn the first tone/click

problem) within 30 sessions.

2.8. Stereological methods

2.8.1. Histology

After completing the behavioral tests, subjects were

deeply anesthetized (10 mg/kg body weight xylazine and

80 mg/kg body weight ketamine) and perfused intracar-

dially with room temperature physiological saline followed

by 200 ml of 4% paraformaldehyde in 0.1 M phosphate

buffer (pH 7.2 at 4 �C). Brains were removed, cryopro-

tected in the same fixative with 30% sucrose at 4 �C, andcut at a thickness of 60 mm in the coronal plane on a

freezing sledge microtome (AO). All sections containing

the prefrontal cortex [21,81] from the frontal pole through

the beginning of the hippocampus were mounted serially

onto gelatin-subbed slides and stained for Nissl substance

with cresyl violet, differentiated, dehydrated, and cover-

slipped with Permount.

2.8.2. Delineation of prefrontal cortex

The mPFC (Fig. 1) was distinguished from surrounding

areas on the basis of cytoarchitectonic criteria of the

prelimbic, dorsal anterior cingulate, and medial precentral

areas [81]. The cardinal feature of the mPFC is an absent

granular layer IV. The rostral boundary is the most rostral

part of the forceps minor of the corpus callosum. The

transition in characteristics of layers I–III and the distinct

features of layers VIa and VIb distinguish the prelimbic area

from the more ventrally located infralimbic area. Dorsal to

the prelimbic area is the dorsal anterior cingulate area

characterized by a more uniform cellular distribution and

the radiations of the shoulder of the corpus callosum.

Dorsolateral to this area along the dorsomedial edge of the

hemisphere is the medial precentral area. It can be distin-

guished from the lateral precentral area based on the

presence of layer IV, a laminated layer V containing more

and larger neurons, a thinner layer VIa with smaller, darker,

and more densely packed cells, and a cell-sparse region

separating layer VIa and VIb, all traits present in the lateral

precentral area. Caudally, the more homogeneous and

denser layer VIa of the ventral anterior cingulate region

distinguishes it dorsally from the dorsal anterior cingulate

area and rostrally from the prelimbic area. The caudal

boundary of mPFC with the retrosplenial cortex is deter-

mined by the appearance of a cell-dense layer II and a

clearly demarcated layer V from a cell-poor layer III in the

most ventral part of the cingulate cortex.

2.8.3. Stereology

Neuronal number was estimated by the optical fractio-

nator method [89,90]. This unbiased stereological approach

involves counting neurons with optical dissectors in a

Fig. 1. Sample low-power photomicrograph of a coronal section through

medial prefrontal cortex stained with cresyl violet. The cortical layers II and

V (where neurons were counted) are delineated with white bars (right).

S.M. Mihalick et al. / Neurotoxicology and Teratology 23 (2001) 453–462456

uniform systematic sample that comprises a known fraction

of the volume of the region being analyzed. It is unaffected

by tissue shrinkage or expansion that occurs during any

tissue preparation. This particular scheme was selected

because it results in mean coefficients of error � .06 and

avoids the need to define explicitly the superficial and deep

boundaries of thin neuronal layers [90].

All slides were coded prior to quantitative procedures

so that experimental treatment group information was not

available until all microscopic analyses were completed.

Video images taken with a Sony camera (Sony, New York,

NY, USA) attached to a Zeiss Axioplan microscope with

100�, 1.4 n.a. oil immersion objective were input to a

Zeiss/Kontron Image Analysis System. Gray level images

were normalized and contour-enhanced before a stand-

ardized counting frame was superimposed.

A neuron was counted if the first recognizable profile of

a neuronal nucleus in focus was within the counting frame.

The 10-mm depth of the counting frame was measured in

0.5-mm steps by the Z-stepping motor of the microscope

automated control unit. The top of the counting frame was

marked from the top of the section by first determining the

first edge of a profile to come into focus and then descend-

ing 10 additional microns before neuronal profiles were

considered to be inside or outside the counting frame.

Total neuronal number in layers II and V of mPFC was

estimated using the following formula:

N ¼X

Q� t

h

1

asf

1

ssf

where N = number of neurons;P

Q � = total number of

neurons counted; t= section thickness (27.50 mm); h = height

of dissector sample (10 mm); asf = area sample fraction = area

of counting frame/area of stepping motor step [(30 mm)2/

(200 mm)2]; and ssf = section sampling fraction = (1/4).

For every fourth section through mPFC (ssf = 1/4),

eight separate measurements of section thickness were

determined by using the 0.5-mm Z-step motor to focus on

the top and subsequently the bottom of each section,

employing the 100� oil immersion objective. Six sections

per animal were used to calculate the mean thickness

(t = 27.50 mm), with a mean coefficient of error = 0.03.

Neuronal counts in each layer were made from a square

counting frame (30� 30 mm) that was sampled every

200 mm within each layer.

3. Results

3.1. Subjects

Table 1 shows characteristics of litters born to dams in

each of the three diet groups. Number of pups did not differ

as a function of sex or treatment (2� 3 ANOVA, P > .05, all

factors). A two-way analysis of variance (ANOVA) indi-

cated that male pups weighed more than female pups on

PD1 [F(1,30) = 8.18, P=.0076], and that mean litter weight

differed according to diet condition [F(2,30) = 4.47, P=.02].

Fisher’s post hoc tests showed that litters exposed prenatally

to ethanol weighed significantly less than litters born to

chow-fed control dams (P < .05), a difference potentially

related to the absolute number of pups per litter. However,

subjects in all groups gained weight steadily (Fig. 2), with

statistically equivalent weight gains in all groups by PD21

(one-way ANOVA, P>.05).

3.2. Pretraining

A one-way ANOVA revealed no significant differences

in the number of sessions to complete pretraining. ETOH,

CHOW, and ISO groups required means of 3.7, 2.5, and 3.0

days, respectively.

3.3. Discrimination learning

3.3.1. Successive discrimination condition

All subjects met the learning criterion for the first

discrimination problem in three to six sessions. No

subject required more than six sessions. A one-way

ANOVA showed that treatment groups did not differ

on the mean number of sessions to acquire the first

discrimination (Fig. 3). Individual subjects learned a

varying number of successive discriminations within the

Table 1

Litter characteristics on PD1

Group Pups Mean n Mean body weight (g)

ETOH Males 8.33 (0.34) 5.93 (0.07)

Females 6.83 (0.24) 5.71 (0.06)

ISO Males 5.83 (0.27) 6.23 (0.08)

Females 7.50 (0.39) 5.80 (0.09)

CHOW Males 5.17 (0.30) 6.56 (0.04)

Females 6.50 (0.46) 6.06 (0.04)

Standard errors of the mean are indicated in parentheses.

Fig. 2. Mean body weight of pups used as subjects in each group, measured

from birth (PD1) to weaning (PD21). S.E.M. values are too small to be

depicted in this figure. All subjects had equivalent weight gains by PD21

(ANOVA, P> .05).

S.M. Mihalick et al. / Neurotoxicology and Teratology 23 (2001) 453–462 457

30-session limit, but a one-way ANOVA determined that

this was unrelated to treatment (P>.05).

3.3.2. Reversal condition: initial discrimination

Treatment groups tended to differ in their proficiency to

acquire a simple discrimination after 30 sessions of experi-

ence with discrimination problems (Fig. 4). One CHOW rat

failed to meet the learning criterion for the tone/click

problem within the 30-session limit, so it was eliminated

from this and all subsequent analyses (i.e., n = 5 for group

CHOW). All other subjects met the learning criterion for the

tone/click problem in two to eight sessions. A one-way

ANOVA revealed a nonsignificant trend for treatment differ-

ences on the number of sessions to learn this problem

[F(2,14) = 3.11, P=.0762].

3.3.3. Reversal condition: transfer of training

Transfer of training was assessed with the tone/click

discrimination reversal — the first occasion in which

positive and negative stimulus functions were reversed.

Subjects met the learning criterion for the tone/click

reversal problem in 3–14 sessions. Fig. 5 shows the mean

number of sessions per group to meet the reversal learning

criterion. A one-way ANOVA revealed a significant treat-

ment effect [F(2,14) = 4.33, P= .0343]. Fisher’s post hoc

analyses demonstrated that ETOH subjects took signi-

ficantly longer than ISO subjects to meet the criterion

[t (14) = 2.88, P = .0120]. The difference between ETOH

and CHOW groups approached significance [t (14) = 1.90,

Fig. 3. Group means ( ± S.E.M.) for the number of sessions to acquire the

initial auditory discrimination. All subjects learned this problem comparably

(ANOVA, P >.05).

Fig. 4. Group means ( ± S.E.M.) for the number of sessions to complete a

tone/click discrimination after 30 sessions of discrimination training. An

ANOVA revealed a nearly significant treatment effect [ F(2,14) = 3.11,

P=.0762].

Fig. 5. Group means ( ± S.E.M.) for the number of sessions to master a

reversal of the tone/click problem. An ANOVA revealed a significant

treatment effect [ F(2,14) = 4.33, P=.0343]. Compared to ISO subjects,

transfer of training was significantly impaired in ETOH subjects ( P < .05).

Compared to CHOW subjects, the deficit in ETOH subjects approached

significance ( P < .08). ISO and CHOW subjects performed comparably

( P >.05).

Fig. 6. Group means ( ± S.E.M.) for the estimated number of neurons in

layers II and V of mPFC. Significant treatment effects were observed for

both layers II [ F(2,14) = 20.22, P < .0001] and V [ F(2,14) = 62.12,

P < .0001]. Compared to groups ISO and CHOW, group ETOH had the

fewest neurons in both layers II ( P< .001 and P < .0001, respectively) and

V ( P < .0001 for both groups).

S.M. Mihalick et al. / Neurotoxicology and Teratology 23 (2001) 453–462458

P=.0781], and performance did not differ between ISO

and CHOW groups.

3.4. Prefrontal cortex measures

Prenatal exposure to ethanol significantly affected the

number of neurons in layers II and Vof mPFC (Fig. 6). One-

way ANOVAs revealed significant treatment differences in

both layers II [ F(2,14) = 20.22, P < .0001] and V

[F(2,14) = 62.12, P < .0001]. Fisher’s post hoc tests com-

paring estimates of neuron number in layer II showed that

ETOH subjects had the fewest neurons when compared to

both ISO [t (14) = 5.09, P=.0002] and CHOW [t (14) = 5.78,

P < .0001] groups. Similarly, estimates for layer V were

lowest for ETOH subjects when compared to both ISO

[t (14) = 8.39, P < .0001] and CHOW [t (14) = 10.45,

P < .0001] groups. Table 2 displays the mean neuronal count

and coefficients of error for each group.

3.5. Brain–behavior relationships

Multiple regressions were calculated to determine

whether performance on any learning measure could be

predicted by treatment group membership or estimated

neuron number in layer II or V of mPFC. A systematic

relationship emerged with the number of sessions required

to learn the tone/click reversal problem [model F(3,13) =

4.14, P=.0289]. The predictor variables accounted for a

large proportion of the variation in performance, with

neuron number in layer V explaining the most (partial

r2 = 33%). A reduced number of neurons in layers II

and V was correlated with a greater number of sessions

to complete the transfer test (rp=� .48, P=.0499 and

rp=� .61, P=.0088, respectively).

4. Discussion

The results provide evidence for selective cognitive

deficits consequent to prenatal ethanol exposure. All sub-

jects performed similarly during both pretraining and

acquisition of the initial discrimination problem. When the

tone/click problem was introduced, a treatment-related dif-

ference in performance approached significance. This trend

suggests that groups may have differed in their ability to

generalize learning within task type, but that the effect may

have been too subtle to be detected with the sample sizes in

this study.

More pronounced deficits became evident on the tone/

click discrimination reversal, which ethanol-exposed sub-

jects learned more slowly than controls. There was a

significant difference between ETOH and ISO groups, the

most important comparison for determining the effects of

ethanol, and a nearly significant difference between ETOH

and CHOW groups. The fact that ISO and CHOW groups

did not differ indicates a lack of nutritional effects on

performance. These data support the hypothesis that prena-

tal ethanol exposure impairs the ability to transfer learning

across task types.

The ETOH deficit on this transfer-of-training test shows

an inflexibility at applying previous experience to a new but

related type of problem. This pattern of emerging deficits is

consistent with the human mental retardation literature

showing that individuals with mental retardation may learn

simple discriminations readily with adequate preparation

and appropriate procedures (e.g., Refs. [19,49,74]), but are

increasingly likely to show cognitive impairments as the

stimuli, contingencies, or relevant training histories grow

more complex [10,11,39,50].

The observed impairments cannot be attributed to

ethanol-induced deficits in motivation, perception, or motor

coordination. All subjects performed comparably during

pretraining, showing that they could move between the

levers within the specified time limits and depress the levers

with sufficient force. Equivalent performance between

groups during the successive discrimination condition indi-

cates that the stimuli were equally discriminable to all

subjects, and that the learning deficits shown by ETOH

subjects later in the experiment were not due to hearing

disorders sometimes associated with prenatal ethanol expo-

sure [12,36].

In addition to the across-task generalization issue, the

transfer test results are also consistent with an impairment

in adaptive response inhibition. While this interpretation is

speculative, the present results are consistent with those

from other studies showing that rats exposed prenatally to

ethanol display learning impairments on tasks requiring

behavioral inhibition. These tasks include tests of avoid-

ance [1,22,63,67,80] and operant DRL schedules that

require low rates of temporally spaced responding

[16,82]. Although accurate performance on all discrimina-

tion problems requires withholding responses to the S�stimulus, reversal problems present the greatest challenge

in this regard because accurate performance directly con-

flicts with recent stimulus–response–consequence contin-

gencies. Thus, ethanol-exposed rats in the current study

may have shown poorer reversal learning because of

difficulty overcoming their prepotent response bias. The

human FAS literature also is in accord. Children prenatally

exposed to alcohol may exhibit executive function deficits

Table 2

Mean neuronal counts and coefficients of error (CE) for stereological

measures of mPFC

Group Layer Mean count Mean CE

ETOH II 51,770.40 0.06

V 31,990.20 0.05

ISO II 73,829.80 0.06

V 47,049.20 0.06

CHOW II 78,046.32 0.06

V 51,672.72 0.06

S.M. Mihalick et al. / Neurotoxicology and Teratology 23 (2001) 453–462 459

related to response inhibition (reviewed in Ref. [34]) and

behavioral control [48].

This study also demonstrated that prenatal ethanol

resulted in reduced cell number in layers II and V of the

mPFC. Moreover, there was a significant correlation

between layer V damage and poor performance on the

transfer test. Thus, it seems likely that the cortical injury

caused by early ethanol exposure contributed to the

observed learning deficits. However, the neurological dam-

age probably was not restricted to the mPFC areas examined

in this study. For instance, early ethanol has adverse effects

on the hippocampus [5,8,26] and the cholinergic system

[7,64]. Moreover, structures important for reversal learning,

such as the amygdala [40] or the nucleus accumbens [76],

also may have been damaged.

The relative contributions of mPFC and non-mPFC

structures to reversal learning cannot be determined from

the data in the current study. However, the observed pattern

of behavioral deficits mirrors that obtained after localized

lesions to the prelimbic and/or infralimbic subregions of the

mPFC incorporated by the limbic circuit [6,20,46]. This

functional similarity suggests that impairments in reversal

learning were at least partly related to the damage sustained

within mPFC.

The neurodevelopmental processes that may have been

affected adversely during the relatively lengthy but critical

period of prenatal exposure in this study include: cell

division, cell commitment, neuronal migration, neurite out-

growth, and glial cell transformation [24,25,27,32,59]. The

timing of exposure appears critical to the CNS region that

will be impacted [32,58,87]. Further research using a

parametric binge model with ethanol exposures timed to

coincide with more discrete stages of cortical histogenesis

may provide additional insight into specific neurodevelop-

mental mechanisms affected by prenatal ethanol exposure.

Acknowledgments

Data collection and manuscript preparation were

supported by NIH grants AA 10688 and HD 04147. We

thank William J. McIlvane, Tom Callahan, Kevin Farren,

and Gerson Tomanari for their contributions to the

development of the project; Darlene Butler for assistance

with histology; Stuart Tobet for assistance with image

analysis; Peter McCaffery for help with spectrophotometry;

and Camilla Symonowicz for assistance with animal

breeding and husbandry.

References

[1] E.L. Abel, In utero alcohol exposure and developmental delay of

response inhibition, Alcohol.: Clin. Exp. Res. 6 (1982) 369–376.

[2] E.L. Abel, An update on incidence of FAS: FAS is not an equal

opportunity birth defect, Neurotoxicol. Teratol. 17 (1995) 437–443.

[3] I. Autti-Ramo, M.L. Granstrom, The psychomotor development

during the first year of life of infants exposed to intrauterine alcohol

of various duration. Fetal alcohol exposure and development, Neuro-

pediatrics 22 (1991) 59–64.

[4] I. AuttiRamo, M.L. Granstrom, The effect of intrauterine alcohol ex-

position in various durations on early cognitive development, Neuro-

pediatrics 22 (1991) 203–210.

[5] D.E. Barnes, D.W. Walker, Prenatal ethanol exposure permanently

reduces the number of pyramidal neurons in rat hippocampus, Brain

Res. 227 (1981) 333–340.

[6] J.T. Becker, D.S. Olton, C.A. Anderson, E.R.P. Breitinger, Cognitive

mapping in rats: The role of the hippocampal and frontal systems in

retention and reversal, Behav. Brain Res. 3 (1981) 1–22.

[7] A.C. Black, L.W. Goolsby, G.A. Cohen, H.E. Young, Effects of pre-

natal ethanol exposure on the hippocampal neurochemistry of albino

rats at 90 days of postnatal age, Am. J. Obstet. Gynecol. 173 (1995)

514–519.

[8] D.J. Bonthius, J.R. West, Permanent neuronal deficits in rats

exposed to alcohol during the brain growth spurt, Teratology 44

(1991) 147–163.

[9] D.J. Bonthius, J.R. West, Early postnatal alcohol exposure acutely and

permanently reduces the number of granule cells and mitral cells in

the rat olfactory bulb: A stereological study, J. Comp. Neurol. 324

(1992) 557–566.

[10] P.H. Brooks, C. McCauley, E. Merrill, Cognition and mental retarda-

tion, in: F.J. Menolascino, J.A. Stark (Eds.), Preventive and Curative

Intervention in Mental Retardation, Brooks, Baltimore, 1987,

pp. 295–320.

[11] J.C. Campione, A.L. Brown, R.A. Ferrara, Mental retardation and

intelligence, in: R.J. Sternberg (Ed.), Handbook of Human Intelli-

gence, Cambridge Univ. Press, Cambridge, 1982, pp. 392–492.

[12] M.W. Church, J.A. Kaltenbach, Hearing, speech, language, and ves-

tibular disorders in the Fetal Alcohol Syndrome: A literature review,

Alcohol.: Clin. Exp. Res. 21 (1997) 495–512.

[13] S.K. Clarren, Neuropathology in fetal alcohol syndrome, in: J.R. West

(Ed.), Alcohol and Brain Development, Oxford Univ. Press, New

York, 1986, pp. 158–166.

[14] C.D. Coles, R.T. Brown, I.E. Smith, K.A. Platzman, S. Erickson,

A. Falek, Effects of prenatal alcohol exposure at school age: I.

Physical and cognitive development, Neurotoxicol. Teratol. 13

(1991) 357–367.

[15] A. Diamond, V. Ciaramitaro, E. Donner, S. Djali, M.B. Robinson,

An animal model of early-treated PKU, J. Neurosci. 14 (1994)

3072–3082.

[16] C.D. Driscoll, J.S. Chen, E.P. Riley, Operant DRL performance in rats

following prenatal alcohol exposure, Neurobehav. Toxicol. 2 (1980)

201–211.

[17] W.V. Dube, T.D. Callahan, W.J. McIlvane, Serial reversals of concur-

rent auditory discriminations in rats, Psychol. Rec. 43 (1993) 429–440.

[18] W.V. Dube, T.D. Callahan, W.J. McIlvane, C.K. Deutsch, M.D.

Ullman, O. Koul, R.H. McCluer, Auditory discrimination reversal

learning and assessment of behavioral teratogenesis in rats, Behav.

Processes 37 (1996) 197–207.

[19] W.V. Dube, R.W. Serna, Reevaluation of a programmed method to

teach generalized identity matching to sample, Res. Dev. Disabil. 19

(1998) 347–379.

[20] A.T. Ferry, X.-C.M. Lu, J.L. Price, Effects of excitotoxic lesions in the

ventral striatopallidal – thalamocortical pathway on odor reversal

learning: Inability to extinguish an incorrect response, Exp. Brain

Res. 131 (2000) 320–335.

[21] P.L. Gabbott, B.G. Dickie, R.R. Vaid, A.J. Headlam, S.J. Bacon, Local

circuit neurones in the medial prefrontal cortex (areas 25, 32 and 24b)

in the rat: Morphology and quantitative distribution, J. Comp. Neurol.

377 (1997) 465–499.

[22] P.V. Gallo, J. Weinberg, Neuromotor development and response in-

hibition following prenatal ethanol exposure, Neurobehav. Toxicol.

Teratol. 4 (1982) 505–513.

S.M. Mihalick et al. / Neurotoxicology and Teratology 23 (2001) 453–462460

[23] C.R. Goodlett, D.J. Bonthius, E.A. Wasserman, J.R. West, An

animal model of central nervous dysfunction associated with fetal

alcohol exposure: Behavioral and neuroanatomical correlates, in:

I. Garmezano, E.A. Wasserman (Eds.), Learning and Memory:

Behavioral and Biological Processes, Erlbaum, Inglewood, NJ,

1992, pp. 183–208.

[24] C.R. Goodlett, S.D. Peterson, K.R. Lundahl, A.D. Pearlman, Binge-

like alcohol exposure of neonatal rats via intragastric intubation induces

both Purkinje cell loss and cortical astrogliosis, Alcohol.: Clin. Exp.

Res. 21 (1997) 1010–1017.

[25] A. Granato, M. Santarelli, A. Sbriccoli, D. Minciacchi, Multifaceted

alterations of the thalamo-cortical– thalamic loop in adult rats prena-

tally exposed to ethanol, Anat. Embryol. (Berlin) 191 (1995) 11–23.

[26] P.L. Greene, J.L. Diaz-Granados, A. Amsel, Blood ethanol concen-

tration from early postnatal exposure: Effects on memory-based learn-

ing and hippocampal neuroanatomy in infant and adult rats, Behav.

Neurosci. 106 (1992) 51–61.

[27] C. Guerri, J. Renau-Piqueras, Alcohol, astroglia, and brain develop-

ment, Mol. Neurobiol. 15 (1997) 65–81.

[28] J.L. Hall, M.W. Church, R.F. Berman, Radial arm maze deficits in rats

exposed to alcohol during midgestation, Psychobiology 22 (1994)

181–185.

[29] J.H. Hannigan, What research with animals is telling us about alcohol-

related neurodevelopmental disorder, Pharmacol., Biochem. Behav. 55

(1996) 489–499.

[30] J.H. Hannigan, E.L. Abel, M.L. Kruger, ‘‘Population’’ characteristics

of birthweight in an animal model of alcohol-related developmental

effects, Neurotoxicol. Teratol. 15 (1993) 97–105.

[31] M.B. Heaton, J.J. Mitchell, M. Paiva, D.W. Walker, Ethanol-induced

alterations in the expression of neurotrophic factors in the developing

rat central nervous system, Dev. Brain Res. 121 (2000) 97–107.

[32] C. Ikonomidou, P. Bittigau, M.J. Ishimaru, D.F. Wozniak, C. Koch,

K. Genz, M.T. Price, V. Stefovska, F. Horster, T. Tenkova, K. Dik-

ranian, J.W. Olney, Ethanol-induced apoptotic neurodegeneration and

fetal alcohol syndrome, Science 287 (2000) 1056–1060.

[33] D.G. Jones, Influence of ethanol on neuronal and synaptic maturation

in the central nervous system morphological investigations, Prog.

Neurobiol. 31 (1988) 171–197.

[34] K. Kaemingk, A. Paquette, Effects of prenatal alcohol exposure on

neuropsychological functioning, Dev. Neuropsychol. 15 (1999)

111–140.

[35] A.C. Kamil, T.B. Jones, A. Pietrewicz, J.E. Mauldin, Positive transfer

from successive reversal training to learning set in blue jays (Cyano-

citta cristata), J. Comp. Physiol. Psychol. 91 (1977) 79–86.

[36] W.M. Kaneko, E.P. Riley, C.L. Ehlers, Electrophysiological and be-

havioral findings in rats prenatally exposed to alcohol, Alcohol 10

(1993) 169–178.

[37] M.E. Kaufman, W.I. Gardner, Transfer of training of learning sets in

mental defectives: I. Discrimination reversal, Am. J. Ment. Defic. 73

(1969) 801–803.

[38] M.E. Kaufman, M.W. Peterson, Acquisition of a conditional discrim-

ination learning set by normal and mentally retarded children, Am. J.

Ment. Defic. 69 (1965) 865–870.

[39] M.E. Kaufman, H.J. Prehm, A review of research on learning sets and

transfer of training in mental defectives, in: N.R. Ellis (Ed.), Interna-

tional Review of Research in Mental Retardation, vol. 2, Academic

Press, New York, 1966, pp. 123–149.

[40] R.W. Kentridge, C. Shaw, J.P. Aggleton, Amygdaloid lesions and

stimulus – reward associations in the rat, Behav. Brain Res. 42

(1991) 57–66.

[41] B. Kolb, Animal models for human PFC-related disorders, in:

H.B.M. Uylings, C.G. Van Eden, J.P.C. De Bruin, M.A. Corner,

M.G.P. Feenstra (Eds.), Progress in Brain Research, vol. 85, Elsevier,

Amsterdam, 1990, pp. 501–519.

[42] L.A. Kotkoskie, S. Norton, Cerebral cortical morphology and behav-

ior in rats following acute prenatal ethanol exposure, Alcohol.: Clin.

Exp. Res. 13 (1989) 776–781.

[43] P.E. Kuhn, M.W. Miller, Expression of p53 and ALZ-50 immuno-

reactivity in rat cortex: Effect of prenatal exposure to ethanol, Exp.

Neurol. 154 (1998) 418–429.

[44] B. Larroque, M. Kaminski, P. Dehaene, D. Subtil, M.J. Delfosse,

D. Querleu, Moderate prenatal alcohol exposure and psychomotor

development at preschool age, Am. J. Public Health 85 (1995)

1654–1661.

[45] M.H. Lee, A. Rabe, Infantile handling eliminates reversal learning

deficit in rats prenatally exposed to alcohol, Alcohol 18 (1999)

49–53.

[46] L. Li, J. Shao, Restricted lesions to ventral prefrontal subareas block

reversal learning but not visual discrimination learning in rats, Phy-

siol. Behav. 65 (1998) 371–379.

[47] N.J. Lobaugh, T. Wigal, P.L. Greene, J.L. Diaz-Granados, A. Amsel,

Effects of prenatal ethanol exposure on learned persistence and hippo-

campal neuroanatomy in infant, weanling and adult rats, Behav. Brain

Res. 44 (1991) 81–86.

[48] S.N. Mattson, A.M. Goodman, C. Caine, D.C. Delis, E.P. Riley,

Executive functioning in children with heavy prenatal alcohol

exposure, Alcohol.: Clin. Exp. Res. 23 (1999) 1808–1815.

[49] W.J. McIlvane, Stimulus control analysis and nonverbal instructional

methods for people with intellectual disabilities, in: N.W. Bray (Ed.),

International Review of Research in Mental Retardation, vol. 18,

Academic Press, San Diego, 1992, pp. 55–109.

[50] W.J. McIlvane, M.F. Cataldo, On the clinical relevance of animal

models for the study of human mental retardation, Ment. Retard.

Dev. Disabil. Res. Rev. 2 (1996) 188–196.

[51] M.W. Miller, Effects of prenatal exposure to alcohol on the distribu-

tion and time of origin of corticospinal neurons in the rat, J. Comp.

Neurol. 257 (1987) 372–382.

[52] M.W. Miller, Effects of prenatal exposure to ethanol on neocortical

development: II. Cell proliferation in the ventricular and subventricu-

lar zones of the rat, J. Comp. Neurol. 287 (1989) 326–338.

[53] M.W. Miller, Effect of early exposure to ethanol on the protein and

DNA contents of specific brain regions in the rat, Brain Res. 734

(1996) 286–294.

[54] M.W. Miller, Effects of prenatal exposure to ethanol on callosal pro-

jection neurons in rat somatosensory cortex, Brain Res. 766 (1997)

121–128.

[55] M.W. Miller, P.E. Kuhn, Cell cycle kinetics in fetal rat cerebral cortex:

Effects of prenatal treatment with ethanol assessed by a cumulative

labeling technique with flow cytometry, Alcohol.: Clin. Exp. Res. 19

(1995) 233–237.

[56] M.W. Miller, G. Potempa, Numbers of neurons and glia in mature rat

somatosensory cortex: Effects of prenatal exposure to ethanol,

J. Comp. Neurol. 293 (1990) 92–102.

[57] M.W. Miller, S. Robertson, Prenatal exposure to ethanol alters the

postnatal development and transformation of radial glia to astrocytes

in the cortex, J. Comp. Neurol. 337 (1993) 253–266.

[58] S.M. Mooney, R.M. Napper, J.R. West, Long-term effect of postnatal

alcohol exposure on the number of cells in the neocortex of the rat: A

stereological study, Alcohol.: Clin. Exp. Res. 20 (1996) 615–623.

[59] D.B. Moore, M.A. Quintero, A.C. Ruygrok, D.W. Walker, M.B. Hea-

ton, Prenatal ethanol exposure reduces parvalbumin-immunoreactive

GABAergic neuronal number in the adult rat cingulate cortex, Neuro-

sci. Lett. 249 (1998) 25–28.

[60] A.H. Nagahara, R.J. Handa, Fetal alcohol exposure alters the induc-

tion of immediate early gene mRNA in the rat prefrontal cortex after

an alternation task, Alcohol.: Clin. Exp. Res. 19 (1995) 1389–1397.

[61] A.H. Nagahara, R.J. Handa, Fetal alcohol exposure produces delay-

dependent memory deficits in juvenile and adult rats, Alcohol.: Clin.

Exp. Res. 21 (1997) 710–715.

[62] J.A. Osborn, C. Yu, K. Gabriel, J. Weinberg, Fetal ethanol effects on

benzodiazepine sensitivity measured by behavior on the elevated plus-

maze, Pharmacol., Biochem. Behav. 60 (1998) 625–633.

[63] V.D. Petkov, E.R. Konstantinova, V.V. Petkov, J.V. Vaglenova, Learn-

ing and memory in rats exposed pre- and postnatally to alcohol. An

S.M. Mihalick et al. / Neurotoxicology and Teratology 23 (2001) 453–462 461

attempt at pharmacological control, Methods Find. Exp. Clin. Phar-

macol. 13 (1991) 43–50.

[64] E.P. Riley, S. Barron, J.H. Hannigan, Response inhibition deficits

following prenatal alcohol exposure: A comparison to the effects of

hippocampal lesions in rats, in: J.R. West (Ed.), Alcohol and Brain

Development, Oxford Univ. Press, New York, 1986, pp. 71–102.

[65] E.P. Riley, L.S. Meyer, Considerations for the design, implementation,

and interpretation of animal models of fetal alcohol effects, Neuro-

behav. Toxicol. Teratol. 6 (1984) 97–101.

[66] E.P. Riley, N.R. Shapiro, E.A. Lochry, Nose-poking and head-dipping

behaviors in rats prenatally exposed to alcohol, Pharmacol., Biochem.

Behav. 11 (1979) 513–519.

[67] G.A. Rockwood, E.P. Riley, Effects of scopolamine on spontaneous

alternation and shuttle avoidance in rats exposed to alcohol in utero,

Alcohol 2 (1985) 575–579.

[68] T.M. Roebuck, S.N. Mattson, E.P. Riley, A review of the neuro-

anatomical findings in children with fetal alcohol syndrome or

prenatal exposure to alcohol, Alcohol.: Clin. Exp. Res. 22 (1998)

339–344.

[69] A.M. Schrier, Transfer by macaque monkeys between the learning

set and repeated reversal tasks, Percept. Mot. Skills 23 (1966)

787–792.

[70] A.M. Schrier, Transfer between the repeated reversal and learning

set tasks: A reexamination, J. Comp. Physiol. Psychol. 87 (1974)

153–156.

[71] R.J. Schusterman, Transfer effects of successive discrimination-rever-

sal training in chimpanzees, Science 137 (1962) 422–423.

[72] R.J. Schusterman, Successive discrimination-reversal training and

multiple discrimination training in one-trial learning by chimpanzees,

J. Comp. Physiol. Psychol. 58 (1964) 153–156.

[73] G.K. Seabold, J. Luo, M.W. Miller, Effect of ethanol on neurotrophin-

mediated cell survival and receptor expression in cultures of cortical

neurons, Brain Res. Dev. Brain Res. 108 (1998) 139–145.

[74] M. Sidman, L.T. Stoddard, Programming perception and learning

for retarded children, in: N.R. Ellis (Ed.), International Review of

Research in Mental Retardation, vol. 2, Academic Press, New York,

1966, pp. 151–208.

[75] K.J. Smith, M.J. Eckardt, The effects of prenatal alcohol on the central

nervous system, Recent Dev. Alcohol. 9 (1991) 151–164.

[76] C.E. Stern, R.E. Passingham, The nucleus accumbens in monkeys

(Macaca fascicularis): III. Reversal learning, Exp. Brain Res. 106

(1995) 239–247.

[77] A.P. Streissguth, H.M. Barr, P.D. Sampson, Moderate prenatal alcohol

exposure: Effects on child IQ and learning problems at age 7 1/2

years, Alcohol.: Clin. Exp. Res. 14 (1990) 662–669.

[78] B.J. Strupp, M. Bunsey, D.A. Levitsky, K. Hamberger, Deficient

cumulative learning: An animal model of retarded cognitive develop-

ment, Neurotoxicol. Teratol. 16 (1994) 71–79.

[79] J.D. Thomas, S.P. Weinert, S. Sharif, E.P. Riley, MK-801 administra-

tion during ethanol withdrawal in neonatal rat pups attenuates ethanol-

induced behavioral deficits, Alcohol.: Clin. Exp. Res. 21 (1997)

1218–1225.

[80] J. Vaglenova, V.V. Petkov, Fetal alcohol effects in rats exposed pre-

and postnatally to a low dose of ethanol, Alcohol.: Clin. Exp. Res. 22

(1998) 697–703.

[81] C.G. van Eden, H.B.M. Uylings, Cytoarchitectonic development of

the prefrontal cortex in the rat, J. Comp. Neurol. 241 (1985) 253–267.

[82] N.S. Vigliecca, S. Fulginiti, S.A. Minetti, Acute ethanol exposure

during pregnancy in rats: Effects upon a multiple learning task,

Alcohol 6 (1989) 63–68.

[83] J.M. Warren, Reversal learning and the formation of learning sets by

cats and monkey, J. Comp. Physiol. Psychol. 61 (1966) 421–428.

[84] S.G. Warren, A.G. Humphreys, J.M. Juraska, W.T. Greenough, LTP

varies across the estrous cycle: Enhanced synaptic plasticity in pro-

estrus rats, Brain Res. 703 (1995) 26–30.

[85] S.G. Warren, J.M. Juraska, Spatial and nonspatial learning across the

rat estrous cycle, Behav. Neurosci. 111 (1997) 259–266.

[86] J.R. West, W.J. Chen, N.J. Pantazis, Fetal alcohol syndrome: The

vulnerability of the developing brain and possible mechanism of dam-

age, Metab. Brain Dis. 9 (1994) 291–322.

[87] J.R. West, C.R. Goodlett, Teratogenic effects of alcohol on brain

development, Ann. Med. 22 (1990) 319–325.

[88] J.R. West, C.R. Goodlett, D.J. Bonthius, K.M. Hamre, B.L. Marcus-

sen, Cell population depletion associated with fetal alcohol brain

damage: Mechanisms of BAC-dependent cell loss, Alcohol.: Clin.

Exp. Res. 14 (1990) 813–818.

[89] M. West, New stereological methods for counting neurons, Neurobiol.

Aging 14 (1993) 275–285.

[90] M.J. West, L. Slomianka, H.J.G. Gundersen, Unbiased stereological

estimation of the total number of neurons in the subdivisions of the rat

hippocampus using the optical fractionator, Anat. Rec. 231 (1991)

482–497.

[91] S.G. Wiener, W.J. Shoemaker, L.Y. Koda, F.E. Bloom, Interaction of

ethanol and nutrition during gestation: Influence on maternal and off-

spring development in the rat, J. Pharmacol. Exp. Ther. 216 (1981)

572–579.

[92] T. Wigal, A. Amsel, Behavioral and neuroanatomical effects of pre-

natal, postnatal, or combined exposure to ethanol in weanling rats,

Behav. Neurosci. 104 (1990) 116–126.

[93] G. Winocur, M. Moscovitch, Hippocampal and prefrontal contribu-

tions to learning and memory: Analysis of lesion and aging effects on

maze learning in rats, Behav. Neurosci. 104 (1990) 544–551.

S.M. Mihalick et al. / Neurotoxicology and Teratology 23 (2001) 453–462462