www.elsevier.com/locate/ynimg

NeuroImage 26 (2005) 536 – 545

Focal brain matter differences associated with lifetime alcohol

intake and visual attention in male but not in female

non-alcohol-dependent drinkers

Eveline A. de Bruin,a,T Hilleke E. Hulshoff Pol,b Hugo G. Schnack,b Joost Janssen,b

Suzanne Bijl,a Alan C. Evans,d J. Leon Kenemans,a,c

Ren2 S. Kahn,b and Marinus N. Verbatena

aDepartment of Psychopharmacology, Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Sorbonnelaan 16,

NL-3584 CA Utrecht, The NetherlandsbDepartment of Psychiatry, Rudolf Magnus Institute, University Medical Center Utrecht, Utrecht, The NetherlandscDepartment of Psychonomics, Helmholtz Institute, Utrecht University, Utrecht, The NetherlandsdMcConnel Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada

Received 27 September 2004; revised 11 January 2005; accepted 26 January 2005

Available online 11 March 2005

AbstractThe purpose of this study was to investigate whether current or

lifetime alcohol intake is related to focal gray and white matter in

healthy non-alcohol-dependent drinkers, and, if so, whether these

densities are related to functional brain activity associated with

visual attention. Voxel-based morphometric analyses of gray- and

white-matter densities, and event-related potentials in response to a

visual-attention task were determined in 47 male drinkers (current

alcohol intake 20 drinks per week, lifetime alcohol intake 240 kg)

and 44 female drinkers (current alcohol intake 15 drinks per week,

lifetime alcohol intake 170 kg). All participants had a negative

personal and family history of alcohol dependence to reduce possible

confounding by genetic factors related to alcohol dependence. In

males, mean lifetime alcohol intake was negatively associated with

gray-matter density and positively associated with white-matter

density in the right frontal gyrus (BA 6) and the right parietal

region (BA 40). Right frontal (but not right parietal) gray and white

matter in males correlated with the P3 amplitude of the event-related

potentials elicited in a visual-attention task. In females, mean lifetime

alcohol intake was not associated with gray- or white-matter density.

Current alcohol intake was unrelated to gray or white matter in

both males and females. In conclusion, lifetime alcohol intake is

associated with focal gray-matter decreases and white-matter

increases in the right frontal and right parietal brain regions in

non-alcohol-dependent males, but not in females. These alcohol-

related differences in focal brain matter in males are associated with

differences in brain function related to visual attention. As the

confounding effects of genetic factors were reduced, the present

1053-8119/$ - see front matter D 2005 Elsevier Inc. All rights reserved.

doi:10.1016/j.neuroimage.2005.01.036

T Corresponding author. Fax: +31 30 253 7900.

E-mail address: E.A.deBruin@pharm.uu.nl (E.A. de Bruin).

Available online on ScienceDirect (www.sciencedirect.com).

results may selectively relate to the effects of alcohol intake on focal

brain matter.

D 2005 Elsevier Inc. All rights reserved.

Keywords: Lifetime alcohol intake; Current alcohol intake; Voxel-based

morphometry; Gray matter; White matter; Event-related potentials; Visual

attention; P3

Introduction

Alcohol-dependent individuals have smaller total-brain and

gray- and white-matter volumes, and greater cerebrospinal-fluid

(CSF) volumes than light drinkers (e.g., Agartz et al., 2003;

Hommer et al., 2001; Pfefferbaum et al., 1992). The effects of

alcohol dependence on brain volumes are regionally specific and

particularly involve frontal-lobe volume decreases (Fein et al., 2002;

Jernigan et al., 1991; Lingford-Hughes et al., 1998; Pfefferbaum et

al., 1997) and neuronal loss (Harper et al., 2003; Kril et al., 1997). In

addition, the effects of alcohol dependence on brain metabolism

(Dao-Castellana et al., 1998; Johnson-Greene et al., 1997) and brain

metabolite concentrations (Meyerhoff et al., 2004; Parks et al.,

2002; Schweinsburg et al., 2001) also vary across brain regions.

There are suggestions that heavy drinkers who are not alcohol-

dependent have ventricular and sulcal widening that is similar to,

but less prominent than that in alcohol-dependent individuals

(Ding et al., 2004; Kubota et al., 2001; Mukamal et al., 2001). It

appears that the effects of alcohol on the brain are also regionally

differentiated in non-alcohol-dependent drinkers, as suggested by a

voxel-based morphometry study on aging in Japanese (Taki et al.,

E.A. de Bruin et al. / NeuroImage 26 (2005) 536–545 537

2004). In this study, habitual alcohol intake was negatively

associated with focal gray matter in frontal and parietal cortices,

and positively associated with focal white matter in the parietal

cortex in males. No significant associations were found in females,

but lifetime alcohol intake of the females was only 12% of that of

the males.

With regard to gender differences in the effects of excessive

alcohol intake brain structure, some studies report that alcohol-

dependent females have greater volume losses than alcohol-

dependent males despite shorter drinking histories and lower or

similar lifetime alcohol intake in the females (Agartz et al., 1999;

Hommer et al., 1996; Hommer et al., 2001). On the other hand,

other studies found that alcohol-dependent females lacked the brain

volume reductions that alcohol-dependent males displayed (e.g.,

Pfefferbaum et al., 2001b; see Hommer (2003) for a discussion).

This discrepancy can possibly be explained by the fact that, in the

studies reporting greater effects of alcohol intake in females, the

females had a higher lifetime alcohol intake per year of their

drinking history than the males. This suggests that alcohol-related

effects on the structure of the brain may be more related to lifetime

alcohol intake corrected for the duration of the drinking history

than to cumulative lifetime alcohol intake or the duration of the

drinking history alone (De Bruin et al., in press).

Alcohol-dependent individuals have worse cognitive perform-

ance than light drinkers (Lawton-Craddock et al., 2003; Parsons,

1994). Though to a lesser extent, heavy drinkers who are not

alcohol-dependent also display cognitive impairment (see Parsons,

1998; Parsons and Nixon, 1998 for a meta-analysis), for instance

with regard to visual attention. Although voxel-based morphom-

etry is not designed to characterize dependencies among different

brain regions (Friston and Ashburner, 2004), it is tempting to

hypothesize that the frontal and parietal differences in focal gray

and white matter found by Taki et al. (2004) are possibly related to

functional abnormalities in the fronto-parietal network for visual

attention (Corbetta and Shulman, 2002). Alcohol-dependent

individuals display reduced event-related potential (ERP) ampli-

tudes during attention tasks (see Farren and Tipton (1999) and

Porjesz and Begeleiter (1996) for a review). In addition, ERP

amplitudes elicited during attention tasks correlate with frontal and

parietal gray matter volumes in healthy subjects (Ford et al., 1994).

Therefore, it would be interesting to investigate whether focal

differences in brain structure related to alcohol intake in non-

alcohol-dependent drinkers can be related to functional brain

activity during a visual-attention task.

Alcohol dependence has a strong genetic component (Enoch,

2003; Schuckit, 2000). Possibly, people with alcohol dependence

or with alcohol-dependent relatives (i.e., with a so-called positive

personal or family history of alcohol dependence) have a different

sensitivity for the toxic effects of alcohol on the brain. Further-

more, different distributions of genetic polymorphisms that code

for enzymes involved in alcohol metabolism result in different

brain alcohol and acetaldehyde levels in Asians as compared to

non-Asians (Quertemont, 2004). As genetic factors related to both

alcohol dependence and enzymatic polymorphisms may have

played a role in the above-mentioned studies, it is unclear to what

extent these findings can be generalized to drinkers with a negative

personal or family history in non-Asian populations.

The purpose of the present study was to examine a possible

relation between current or lifetime alcohol intake and focal gray

and white matter in a Dutch sample of male and female drinkers

with a negative personal and family history of alcohol dependence.

In addition, in case of alcohol-related differences in gray and/or

white matter, possible functional consequences of these brain matter

differences with regard to visual attention were explored. Cerebral

gray- and white-matter densities were estimated in 91 healthy male

and female drinkers using voxel-based morphometric analyses of

high-resolution magnetic resonance images (Ashburner and Friston,

2000, 2001). Functional brain activity related to visual attention was

investigated by measuring event-related potentials in reaction to a

visual-attention task in the same sample. The relationship between

structural and functional differences in the brains of non-alcohol-

dependent drinkers was explored by correlating brain matter

densities related to alcohol intake with P3 amplitudes of event-

related potentials elicited in the visual-attention task.

Methods

Participants

Participants were recruited via newspaper advertisements, and

were paid for their participation. After written and oral explanation

of the study, they signed the informed consent, and filled in an

extensive questionnaire on physical and mental health. Eligible

subjects were invited for a 3-h screening consisting of a physical

check-up and a structured interview assessing for the presence of

psychopathological symptoms (the Composite International Diag-

nostic Interview (Robins et al., 1988), based on the DSM-IV).

From about 1500 applications, 96 healthy Caucasian non-depend-

ent drinkers (50 men, 46 women) drinking from one standard drink

(i.e., 100 cc wine, 250 cc beer, or 30 cc spirits, equivalent to 12 g

of alcohol per drink) per 2 weeks up to 53 standard drinks per week

were selected to participate in the study. Total abstainers were not

included as they have worse cognitive performance (Britton et al.,

2004; Elias et al., 1999; Kalmijn et al., 2002) and a higher risk for

coronary heart disease (Abramson et al., 2001; Corrao et al., 2004)

and ischemic stroke (Berger et al., 1999; Djouss2 et al., 2002;

Sacco et al., 1999; Suter and Vetter, 1999) than light-to-moderate

drinkers, which might confound the effects of alcohol intake on the

brain.

Each participant was right-handed as determined with the

Edinburgh Handedness Inventory, and had a blood pressure and

resting heart rate within normal limits. The electrocardiogram,

hematology, and blood chemistry were screened for abnormalities

by a medical specialist. Education was assessed as the number of

years of formal education from primary school onwards. Premorbid

IQ was estimated with the Dutch Adult Reading Test (Nederlandse

Leestest voor Volwassenen, the Dutch version of the National

Adult Reading Test; Schmand et al., 2003). The participants had no

(history of) chronic somatic or neurological disease, head trauma

or loss of consciousness for more than 10 min, or psychiatric

disease at any point in life. Other exclusion criteria were: use of

psychoactive medication within the past month, drug use (besides

alcohol) for more than three times in life, total alcohol abstinence,

and first- or second-degree relatives with neurological or psychi-

atric deficits. Every effort was made to ensure via the questionnaire

and the interview that all participants had a negative family history

of alcoholism up to the second degree. The main reasons for

exclusion were (a history of) psychiatric symptoms (21%, mostly

major depression at some point in life), use of psychoactive

medication within the past month (19%), chronic somatic or

neurological disease (13%), drug use more than three times in life

E.A. de Bruin et al. / NeuroImage 26 (2005) 536–545538

(9%), first- or second-degree relatives with alcoholism (8%), first-

or second-degree relatives with neurological or psychiatric deficits

(7%), head trauma or loss of consciousness for more than 10 min

(5%), or conditions that preclude MRI scanning (3%).

Lifetime alcohol intake (cumulative lifetime alcohol intake in

kg) and duration of the drinking history (in years) were assessed by

a questionnaire in Dutch, based on the Lifetime Drinking History

interview (Lemmens et al., 1997; Skinner and Sheu, 1982). To

estimate current alcohol intake, subjects filled in a diary during a

two-week period in which they recorded the number of alcoholic

drinks and the type of glass, bottle, or can on a daily basis.

Reported alcohol intake was converted into number of standard

drinks (i.e., units containing 12 g of alcohol) per week. To achieve

a homogeneous distribution of age, gender, Mini Mental State

Examination (MMSE) score and body-mass index, the drinkers

were stratified on the basis of the diary. The following drinking

levels were defined: �5 alcoholic drinks per week (14 men, 16

women), 6–20 alcoholic drinks per week (17 men, 15 women),

and 21–60 alcoholic drinks per week (19 men, 15 women). The

cut-off of 21 drinks per week was based on the results of a meta-

analysis by Parsons (1998), demonstrating that non-alcohol-

dependent drinkers with an alcohol intake of 21 or more alcoholic

drinks per week had worse cognitive functioning than drinkers

consuming less than 21 drinks per week. For five participants (one

male and one female drinking �5 drinks per week, and two males

and one female drinking 21–60 drinks per week), the quality of the

magnetic resonance imaging (MRI) scans was too poor for a

satisfying separation of gray and white matter; these participants

were removed from all analyses. For a summary of the

demographics of the 91 participants in the final analyses, see

Table 1.

This study was approved by the Utrecht University medical

ethics committee, and all participants were treated in accordance

with the Helsinki Declaration and amendments.

MRI scan acquisition and analysis

Magnetic resonance images were acquired using a 1.5-T Philips

NT scanner (Philips Medical Systems, Best, the Netherlands). T1-

weighted three-dimensional fast-field echo scans with 160–180

1.2-mm contiguous coronal slices (TE = 4.6 ms, TR = 30 ms, flip

angle 30-, field of view 256 mm/80%; in-plane voxel sizes 1 � 1

Table 1

Demographics (mean, standard deviation in brackets)

Males Females

n 47 44

Age (years) 49.5 (8.7) 49.9 (6.9)

Education 14.0 (2.5) 12.4 (2.6)

IQ 108.1 (7.4) 103.8 (9.4)

Body Mass Index (kg/m2) 25.0 (2.7) 24.8 (3.3)

Lifetime Alcohol Intake (kg) 239.7 (199.0) 170.0 (152.7)

Duration of Drinking History (years) 33.1 (8.0) 32.5 (7.3)

Mean Lifetime Alcohol Intake

(kg/year)

7.0 (5.5) 5.2 (4.3)

Mean Lifetime Alcohol Intake

(units per week)a11.2 (8.8) 8.3 (6.9)

Current Alcohol Intake

(units per week)a19.7 (15.7) 15.4 (14.1)

a Number of standard drinks per week; a standard drink contains about 12 g

of pure alcohol.

mm2), and T2-weighted dual-echo TSE scans with 120 1.6-mm

contiguous coronal slices (TE1 = 14 ms, TE2 = 80 ms, TR = 6350

ms, flip angle 90-, FOV = 256 mm/80%; in-plane voxel sizes 1 � 1

mm2) were used. In addition, a T2-weighted turbo-spin dual-echo

scan was made for diagnostic evaluation by independent clinical

radiologists. All scans were free of pathology such as infarctions,

chronic ischemic change, or any other brain disease, to ensure the

accuracy of the analyses of the MRI scans.

Processing was done on the neuro-imaging computer network

of the Department of Psychiatry at the University Medical Center

Utrecht, the Netherlands. All scans were coded to ensure

investigator blindness to subject identification and drinking level.

The T1-weighted images were put into Talairach orientation (no

scaling) and corrected for scanner RF field nonuniformity (Sled et

al., 1998). Intensity histogram analysis yielded thresholds for

separating brain tissue from cerebrospinal fluid and, within the

brain, gray matter from white matter. Gray- and white-matter

segments were created by applying these thresholds to the images

(see Schnack et al., 2001). The segments were blurred by a 3D

Gaussian kernel (full width at half maximum of 8 mm) in order to

gain statistical power. The values of these blurred gray- and white-

matter segments reflect the local presence or concentration of gray

or white matter per voxel, and range from 0 to 1 in arbitrary units.

These images are referred to as Fdensity maps._In order to compare brain tissue at the same anatomical location

in all subjects, the gray- and white-matter segments were trans-

formed into a standardized coordinate system. These transforma-

tions were calculated in two steps. First, the T1-weighted images

were linearly transformed to the model brain, the previously

determined Fmost average_ brain (Hulshoff Pol et al., 2001). In this

linear step, a joint entropy mutual information metric was

optimized (Maes et al., 1997). In the second step, nonlinear

(elastic) transformations were calculated to register the linearly

transformed images to the model brain up to a scale of 4 mm full

width at half maximum, thus removing global shape differences

between the brains, but retaining local differences. For this step,

the program ANIMAL (Collins et al., 1995) was used. The gray-

and white-matter density maps were transformed to the model

space by applying the concatenated linear and nonlinear trans-

formations. Finally, the maps were resampled to voxels of size 2 �2 � 2.4 mm3. Similar analyses were applied in studies with

schizophrenic patients (Hulshoff Pol et al., 2001, 2004) and

habitual drinkers (Taki et al., 2004).

Event-related potential acquisition and analysis

The visual-attention task (validated by Kenemans et al. (1993,

1995) and Kok et al. (1995), and applied in Baas et al. (2002) and

Kenemans et al. (2000, 2002) consisted of the presentation of four

different black and white gratings (15 � 11.5 cm) presented on a

gray background on a monitor at a distance of 1 m (8.6 � 6.6

cycles per degree (cpd)). The gratings consisted of thin (0.8 cpd) or

thick (3.2 cpd) lines that were presented in a horizontal or vertical

orientation. Stimulus duration was 50 ms, and the inter-stimulus

interval varied between 750 and 1000 ms. The participants were

instructed to press a button as fast as possible with the right-hand

index finger whenever a target stimulus appeared. Target stimuli

(25%) differed from non-target stimuli (75%) in line orientation

and width; stimuli were target or non-target depending on the

instruction. All stimuli were randomly mixed in two blocks of 300

stimuli each.

E.A. de Bruin et al. / NeuroImage 26 (2005) 536–545 539

The electroencephalogram (EEG) was recorded during execu-

tion of the visual-attention task from 62 tin electrodes placed

according to the international 10–10 system with the left mastoid

as a reference and AFz as a ground (QuikCap, Neurosoft, El Paso,

USA). The EEG signal was filtered online between 0.10 and 50

Hz, and digitized at a rate of 250 Hz. Off-line, the signal was

filtered with a 30-Hz low-pass filter, and re-referenced to an

average reference. Epochs of 1000 ms from 100 ms before stimulus

onset to 900 ms after stimulus onset were created, of which the first

100 ms served as a baseline. Based on Kenemans et al. (1995), the

P3 amplitude was determined at the Pz electrode on the peak of the

difference wave (target stimuli minus non-target stimuli with the

same line orientation) between 390 and 440 ms post-stimulus.

More information about the acquisition and analysis of ERP data,

and about the relationship between alcohol intake and the N1, N2b,

and P3 amplitudes can be found in Bijl et al. (submitted). Here,

only the association between the amplitude of the P3 component

and alcohol-related brain matter differences possibly found by

voxel-based morphometry are reported.

Statistical analyses

Mean lifetime alcohol intake was calculated by dividing

cumulative lifetime alcohol intake by the number of years of

drinking. The data were analyzed for outliers, normality of the

distribution, and homogeneity of the variance. The data of current

alcohol intake, mean lifetime alcohol intake, and cumulative

lifetime alcohol intake were square-root transformed to achieve a

normal distribution and variance homogeneity. Multiple univariate

linear regression analyses were performed for each voxel sepa-

rately through all gray- and white-matter density maps, with

densities as dependent variables. Age, gender, and alcohol intake

(current alcohol intake, mean lifetime alcohol intake, or cumulative

lifetime alcohol intake) were entered into the analyses as predictor

variables. To evaluate interactions with gender, the interaction

between gender and alcohol intake was added as a predictor

variable.

Results from the regression analyses were expressed as

unstandardized regression coefficients B with a standard error

SEB and an intercept. The relative importance of the predictors in

the model was indicated by a t statistic and a corresponding P

value. The statistical significance level was corrected for multiple

comparisons according to random field theory (Worsley et al.,

1996). The critical t value corresponding to an alpha-level of 0.05,

for a search volume of 1350 cm3, a blur kernel of 8 mm, and 86

degrees of freedom, was )t) > 5.30. The predictability of the

Table 2

Focal gray- and white-matter density differences at the local maxima associated w

drinkersa

Area Gray matter

Name Brodmann df Density Association with

Mean (SE) B (SEB)

Right Frontal Gyrus area 6 86 0.179 (0.015) �0.060 (0.011)

Right Parietal Region area 40 86 0.448 (0.021) �0.084 (0.015)

a Mean gray- and white-matter densities in males (varying between 0 and 1; stan

densities and mean lifetime alcohol intake (mean LAI) in males after controlling fo

�kg/y; standard error SEB in brackets), and t values )t) > 5.315 (P < 0.05) with 86

mm from the anterior commissure to the left (�) or the right (+) side (X), to the po

(+) side of the brain (Z).

complete regression models (i.e., the proportion of variance

explained) was reflected by the R2 value.

In case of a relationship between alcohol intake and brain

matter density, the association between the gray- and white-matter

densities at the local maxima and the amplitude of the P3 ERP

component elicited in the visual-attention task was explored.

Associations were determined by means of Pearson’s product-

moment correlations with a two-tailed significance level of P <

0.05. ERP data of two males and two females were discarded

because of technical problems.

Results

Current alcohol intake correlated strongly with lifetime alcohol

intake in both males (r45 = 0.752; P < 0.001) and females (r42 =

0.778; P < 0.001). Alcohol intake was unrelated to IQ in both

males (current alcohol intake: r44 = 0.131; n.s.; lifetime alcohol

intake: r44 = 0.078; n.s.) and females (current alcohol intake: r41 =

�0.163; n.s.; lifetime alcohol intake: r41 = �0.222; n.s.). Alcohol

intake was also unrelated to education in both males (current

alcohol intake: r45 = �0.063; n.s.; lifetime alcohol intake: r45 =

�0.241; n.s.) and females, except for current alcohol intake

(current alcohol intake: r41 = 0.341; P = 0.025; lifetime alcohol

intake: r41 = 0.182; n.s.).

Current alcohol intake was unrelated to gray- or white-matter

densities in either males or females. In the females, mean lifetime

alcohol intake was not related to gray- or white-matter densities

either. In the right frontal gyrus (BA 6), the association between

mean lifetime alcohol intake and brain matter densities differed

significantly between males and females for gray (regression slope

B = 0.061 �kg/year, SEB = 0.011; t86 = 5.756; X = 21, Y = �2, Z =

41) and white matter (regression slope B = �0.055 �kg/year, SEB =

0.010; t86 = �5.417; X = 21, Y = �5, Z = 41).

In male drinkers, higher mean lifetime alcohol intake was

associated with a lower gray-matter density (intercept 0.309; B =

�0.060 �kg/year; SEB = 0.011; t86 = �5.569; R2 = 0.301; see

Table 2) and a higher white-matter density (intercept 0.694; B =

0.061 �kg/year; SEB = 0.011; t86 = 5.505; R2 = 0.297) in the right

frontal gyrus (Brodmann area (BA) 6; X = 21, Y = �2, Z = 43; see

Fig. 1a), and with a lower gray-matter density (intercept 0.799; B =

�0.084 �kg/year; SEB = 0.015; t86 = �5.691; R2 = 0.338) and a

higher white-matter density (intercept 0.169; B = 0.095 �kg/year;

SEB = 0.018; t86 = 5.396; R2 = 0.308) in the right parietal region

(BA 40; X = 41, Y = �48, Z = 37; see Fig. 1b). In Figs. 2a and b,

the statistical t value maps have been superimposed on sections of

ith mean lifetime alcohol intake (mean LAI) in male non-alcohol-dependent

White matter

mean LAI Density Association with mean LAI

t R2 Mean (SE) B (SEB) t R2

�5.569 0.301 0.818 (0.015) 0.061 (0.011) 5.505 0.297

�5.691 0.338 0.516 (0.024) 0.095 (0.018) 5.396 0.308

dard error SE in brackets), the association between gray- and white-matter

r age and gender, expressed as unstandardized regression coefficients B (in

degrees of freedom (df). The Talairach coordinates indicate the distance in

sterior (�) or the anterior (+) side (Y), and to the inferior (�) or the superior

Fig. 2. (a and b) Lower gray-matter densities in the right frontal gyrus (BA

6, a) and in the right parietal region (BA 40, b) with increasing mean

lifetime alcohol intake in male drinkers. The statistical t value map of gray-

matter density (thresholded at )t) > 4 for illustrative purposes) is

superimposed on an axial (top), sagittal (middle) and coronal (bottom)

section of the standardized reference brain. Talairach coordinates of a: X =

21; Y = �2; Z = 43. Talairach coordinates of b: X = 41; Y = �48; Z = 37.

The t value maps of the white-matter densities have reversed signs but are

otherwise similar to those of the gray-matter densities. The right side of the

axial and coronal slices depicts the right side of the brain.

Fig. 1. (a and b) Relationship of focal gray matter density with mean

lifetime alcohol intake (lifetime alcohol intake divided by the duration of

the drinking history) in the right frontal (Brodmann area 6; a) and right

parietal (Brodmann area 40; b) brain areas in males and females. The

unstandardized residuals reflect the difference between the observed gray-

matter densities and the densities expected on the basis of the regression of

age and gender on gray-matter densities for males (n = 47; dark squares)

and females (n = 44; light circles). The lines indicate the regression slopes

of gray-matter densities for mean lifetime alcohol intake in males (solid

line) and females (dashed line) after correction for age and gender. As the

distribution of mean lifetime alcohol intake (x axis) was normalized by

square-root transformation, the residuals are plotted against a square-root

transformed x axis.

E.A. de Bruin et al. / NeuroImage 26 (2005) 536–545540

the standardized reference brain for an illustration. The intra-class

correlations for the VBM analysis, determined on the basis of scans

of subjects from another sample who were scanned twice (Schnack

et al., in preparation), were 0.95 and higher in the significant

voxels.

Adding IQ, education, MMSE score, or body-mass index to the

regressions did not improve the predictability of the regression

models as it marginally increased the R2 values in some (but not

all) cases, but always lowered the F test statistic for the complete

regression model. The F value of the original model for the right

frontal region was F4,64 = 9.28 for gray matter and F4,64 = 9.07 for

white matter. Adding IQ, education, MMSE score, or body-mass

index as predictors to the model lowered the F values with 1.75 to

2.21. The F value of the original model for the right parietal region

was F4,64 = 10.97 for gray matter and F4,64 = 9.58 for white matter.

Adding IQ, education, MMSE score, or body-mass index as

predictors to the model lowered the F values with 0.65 to 2.16. F

test statistics indicate whether increments in predictability (R2) by

adding more predictors to a regression model is Fworth_ the cost oflosing degrees of freedom (see pages 483–486 in Hays, 1981).

Cumulative lifetime alcohol intake (i.e., lifetime alcohol intake

not corrected for the duration of the drinking history) was related

to gray- and white-matter densities in a way similar to mean

lifetime alcohol intake, that is, gray-matter densities were lower

and white-matter densities were higher in the right frontal gyrus

(BA 6) and right parietal region (BA 40) with higher cumulative

lifetime alcohol intake in males. As compared to mean lifetime

alcohol intake, cumulative lifetime alcohol intake had similar t

values in the right parietal region (gray matter: t86 = �5.683,

white matter: 5.424), but lower t values in the right frontal gyrus

(gray matter: t86 = �5.507, white matter: t86 = 5.424). No

relationship between gray- and white-matter densities and cumu-

lative lifetime alcohol intake was found in the females.

The intercepts and regressions slopes indicate that the average

male drinker of about 50 years of age consuming one glass of wine,

beer or spirits per week (equivalent to 52 weeks/year � 12 g/day =

0.62 kg/year) during his adult life has a gray-matter density of 0.309

(intercept) + (�0.060 � �(0.62)) (the effect of alcohol on gray-

matter density in right BA 6) + 0.0003 � 50 (the effect of age on

gray-matter density in right BA 6) = 0.277 in his right frontal gyrus.

A male drinker of similar age drinking seven glasses of alcohol per

week lifetime (equivalent to 365 days/year � 12 g/day = 4.38 kg/

year) has a gray-matter density of 0.309 + (�0.06 � �(4.38)) +

0.0003� 50 = 0.199, which is 28% lower than the male consuming

one drink per week. Likewise, the man consuming seven drinks per

E.A. de Bruin et al. / NeuroImage 26 (2005) 536–545 541

week lifetime has an 11% higher white-matter density in his right

frontal gyrus (BA 6), a 19% lower gray-matter density and a 34%

higher white matter in his right parietal region (BA 40) at the local

maxima as compared to the man drinking one alcoholic consump-

tion per week lifetime.

The P3 amplitude was 3.22 T 0.30 AV (mean T standard error)

in males and 3.36 T 0.32 AV in females. In the male drinkers, gray-

matter densities in BA 6 correlated negatively (r43 = �0.324; P =

0.030) and white-matter densities in BA 6 correlated positively

(r43 = 0.325; P = 0.030) with the amplitude of the P3 (see Fig. 3),

whereas gray- and white-matter densities in BA 40 did not

correlate with the P3 amplitude (r43 = �0.114 and 0.150,

respectively). The P3 amplitude did not correlate with mean

lifetime alcohol intake in males (r43 = �0.008; n.s.). When mean

lifetime alcohol intake was partialled out, the correlation between

brain matter densities in BA 6 and the P3 amplitude became more

significant (gray matter r42 =�0.439; P = 0.003; white matter r42 =

0.437; P = 0.003), while the correlations between brain matter

densities in BA 40 and the P3 amplitude remained insignificant

(gray matter r42 = �0.160, n.s.; white matter r42 = 0.197, n.s.). In

the female drinkers, gray- and white-matter densities in right BA 6

and right BA 40 did not correlate with the P3 amplitude (right BA

6: gray matter r40 = 0.101, n.s.; white matter r40 = �0.095, n.s.;

right BA 40: gray matter r40 = �0.135, n.s.; white matter r40 =

0.079, n.s.).

Discussion

The present study investigated the relationship between current

or lifetime alcohol intake and focal gray and white brain matter and

visual attention in 47 male and 44 female healthy non-alcohol-

dependent drinkers. In females with a current alcohol intake of 15

drinks per week and a lifetime alcohol intake of 170 kg, gray and

white matter were unrelated to either current or mean lifetime

alcohol intake. In males with a current alcohol intake of 20 drinks

per week and 240 kg lifetime, current alcohol intake was also

unrelated to gray or white matter. Lifetime alcohol intake was

negatively associated with gray matter and positively associated

Fig. 3. Correlations between gray-matter densities (gray squares, trendline

with long strokes) and white-matter densities (white squares, trendline with

short strokes) in the right frontal gyrus (Brodmann area 6) and the P3

amplitude of the ERP elicited in a visual-attention task in males.

with white matter in the right frontal gyrus (Brodmann area (BA)

6) and the right parietal region (BA 40). Lifetime alcohol intake

weakened the association between gray- and white-matter densities

in the right frontal gyrus and the amplitude of event-related

potentials recorded during a visual-attention task in the same male

participants. The present results suggest that, for example, a 50-

year-old male with a mean lifetime alcohol intake of seven

alcoholic drinks per week has on average 28% less gray-matter in

right BA 6 and 19% less gray-matter in right BA 40, and 11% more

white matter in right BA 6 and 34% more white matter in right BA

40 at the local maxima, as compared to a peer drinking only one

glass of alcohol per week.

The association of higher lifetime alcohol intake with less gray

matter and more white matter in males, and the absence of such a

relationship in females is in agreement with the results of Taki et

al. (2004). In addition, the localization of alcohol-related gray-

matter differences in the right frontal brain area was replicated.

However, the present gray-matter differences are less widespread

than in the Japanese aging study, as we did not find a relationship

between alcohol intake and gray matter in the left middle occipital

gyrus, left precentral gyrus, left middle inferior gyrus, left

postcentral gyrus, and left cuneus, or white matter in the left

postcentral gyrus. There may be several reasons for this discrep-

ancy. Firstly, the Japanese sample may have contained individuals

with different genetic polymorphisms coding for enzymes

involved in alcohol metabolism, which may be associated with a

different impact of alcohol intake on the brain. Secondly, Taki

included participants up to 79 years of age, while our oldest

participant was 62 years old. As the effects of long-term alcohol

use on brain structure interact with age (Pfefferbaum et al., 1992,

1993, 1997), the effect of alcohol intake may have been greater in

the older Japanese participants. Thirdly, the discrepancy may be

due to the fact that we excluded individuals with a family history

of alcohol dependence.

As the purpose of the study by Taki et al. (2004) was to

evaluate the effects of aging in the context of cerebrovascular risk

factors, the Japanese sample also included individuals with medical

conditions like hypertension, diabetes mellitus and hypercholester-

olemia. In contrast, the current sample was selected to be free of

individuals with any chronic somatic disease, medicated or not, in

order to study the ‘‘pure’’ effects of lifetime alcohol intake on the

brain. Therefore, another reason for the more regionally confined

effect of alcohol intake may be the absence of physical and mental

comorbidity in the present sample. Thus, our findings suggest that

the pure effects of long-term alcohol intake on focal brain matter

are subtle. We would like to emphasize that, in spite of the

relatively limited effects of long-term alcohol intake on brain

matter in non-alcohol-dependent drinkers, we do not suggest that

alcohol consumption is harmless, as alcohol intake is clearly

related to disease states such as certain types of gastro-entero-

logical cancers (e.g., Corrao et al., 2004; Longnecker, 1995).

Interestingly, the relationship between lifetime alcohol intake

and brain matter in male drinkers was confined to BA 6 and 40.

These areas are part of a fronto-parietal network (Cavada and

Goldman-Rakic, 1989), which presumably underlies the visual-

attention system (Corbetta and Shulman, 2002). Gray matter

density in BA 6 was negatively correlated with the amplitude of

the ERP component related to visual attention. In addition, a

complementary positive correlation between the P3 amplitude and

white matter in BA 6 was found. No correlation between right

parietal gray and white matter and the P3 amplitude was found.

E.A. de Bruin et al. / NeuroImage 26 (2005) 536–545542

The current finding of a correlation between frontal gray matter

and ERP amplitudes in an attention task corresponds with the study

of Ford et al. (1994). However, the direction of the correlation

between frontal gray matter and P3 amplitude was different, and

the correlation with parietal gray matter was not found. These

differences may be related to differences in the modality in which

the ERP stimuli were presented (visual instead of auditory), or to

the type of MRI analysis (focal brain structure as opposed to global

brain volumes).

Partialling out the effects of mean lifetime alcohol intake made

the correlations between frontal gray and white matter and the P3

amplitude stronger. The fact that mean lifetime alcohol intake had

this effect while only correlating with brain matter densities in BA

6 and not with the P3 amplitude, suggests that alcohol intake may

have influenced the P3 amplitude only indirectly through its

relationship with brain matter densities in BA 6. This may indicate

that lifetime alcohol intake influences the relationship between

frontal gray and white matter and functional brain activity related

to visual attention in non-alcohol-dependent drinkers.

The finding of a higher white-matter density with greater

lifetime alcohol intake is in agreement with our earlier

volumetric finding of greater frontal white-matter volume with

higher mean lifetime alcohol intake in the same sample of male

non-alcohol-dependent drinkers (De Bruin et al., in press). The

second parallel with the volumetric study is that mean lifetime

alcohol intake was associated with brain densities in the same

way as cumulative lifetime alcohol intake (i.e., in the same brain

areas and in males only), but that this association was stronger

for mean lifetime alcohol intake than for cumulative lifetime

alcohol intake, particularly in the right frontal gyrus. This

substantiates our hypothesis that lifetime alcohol intake corrected

for the duration of the drinking history may be a stronger

indicator for differences in brain structure than uncorrected

lifetime alcohol intake.

It is not clear why the effects of lifetime alcohol intake in this

study are limited to the right hemisphere. Lowering the critical

threshold t value for exploratory reasons did not reveal a similar

pattern in the left hemisphere, as illustrated in Figs. 2a and b. For

these figures, the threshold was set to )t) > 4 for illustrative

purposes, while the critical )t) was 5.30. These figures illustrate

that there was no mirror effect on the left side of the brain.

With regard to the interpretation of more white matter with

higher lifetime alcohol intake, one can only speculate. A possible

explanation is that more white matter reflects a higher number of

glial cells, which are associated with repair processes in the brain

(Hansson and RPnnb.ck, 2003). This would be in accordance with

a post-mortem study in which alcohol-dependent individuals were

reported to have a higher number of glial cells in the superior

frontal cortex (Harper et al., 1987). Alternatively, more white

matter could be a sign of more axonal sprouting (Chen et al.,

2002), possibly related to functional reorganization.

In the female drinkers, no association between lifetime alcohol

intake and gray or white matter was found. This may be related

to the slightly lower cumulative and mean lifetime alcohol intake

of the female drinkers. However, the existence of many other

gender differences with respect to alcohol, such as a more

beneficial effect of light and moderate alcohol intake on cognition

in females than in males (Britton et al., 2004; Dufouil et al.,

1997; Elias et al., 1999), suggests a genuine gender difference in

the long-term effects of alcohol intake on the brains of non-

alcohol-dependent drinkers. Possible behavioral explanations are

differences in drinking patterns or beverage choice between males

and females (Green et al., 2004). Biological explanations are

gender differences in alcohol metabolism (Graham et al., 1998;

Mumenthaler et al., 1999), lower sensitivity of brain metabolism

to acute alcohol effects in females (Wang et al., 2003), or a

protective effect of estrogen against glutamate toxicity in neuro-

nal and glial cells (Shy et al., 2000; Singer et al., 1996). On the

other hand, alcohol intake could be a proxy marker for good

physical and mental health in non-alcohol-dependent females

(Britton et al., 2004; Dufouil et al., 2001).

In recently detoxified alcoholics, CSF volume decreases

(Pfefferbaum et al., 1995; Schroth et al., 1988; Shear et al.,

1994; Zipursky et al., 1989) and gray- and white-matter volume

increase (Pfefferbaum et al., 1995; Shear et al., 1994) relatively

shortly after the onset of abstinence. This recovery of brain volume

in abstinent alcoholics as compared to relapsed alcoholics can be

measured as early as 3 to 5 weeks after the onset of sobriety, while

over the same interval brain volumes of control groups remain

virtually unchanged. This suggests that changes in brain structure

related to alcohol intake occur relatively fast. In the current sample

of non-alcohol-dependent drinkers, gray and white matters were

not associated with current alcohol intake. This may be explained

by the fact that brain recovery in abstinent alcoholics is a process

that is different from brain damage in active non-alcohol-depend-

ent drinkers. Alternatively, non-alcohol-dependent drinkers with-

out alcohol-dependent relatives, having a low influence of genetic

factors related to alcohol dependence, may have a different brain

reaction to recent alcohol intake than alcohol-dependent individ-

uals with a high influence of those genetic factors.

This study is limited in that the exploration of functional

consequences of alcohol-related differences in focal brain matter

was confined to brain activity related to visual attention. Brodmann

areas 6 and 40 are also involved in motor activity (Fink et al.,

1997) and visual working memory (Tsukiura et al., 2001). In

previous studies, alcohol-dependent individuals showed lower

functional activation of, among other regions, BA 6 and 40 during

a visual working memory task (Pfefferbaum et al., 2001; Tapert et

al., 2001). Therefore, associations between focal brain matter and

visual working memory or motor activity in non-alcohol-depend-

ent drinkers could complement the present findings on visual

attention. Furthermore, we are well aware of the methodological

limitations of voxel-based morphometry concerning spatial nor-

malization and smoothing of the data and statistical issues

(Ashburner and Friston, 2000, 201; Bookstein, 2001; Davatzikos,

2004). However, the present associations between lifetime alcohol

intake and brain matter densities are conservative due to stringent

corrections for multiple comparisons based on the random-field

theory (Worsley et al., 1996).

In conclusion, lifetime alcohol intake is negatively associated

with focal gray matter and positively associated with focal white

matter in the right frontal gyrus (BA 6) and the right parietal region

(BA 40) in male non-alcohol-dependent drinkers. Lifetime alcohol

intake attenuates the relationship between right frontal gray and

white matter and the P3 amplitude, indicating an influence of

alcohol intake on brain function via its relation with brain structure

in the right frontal gyrus in male non-alcohol-dependent drinkers.

In female non-alcohol-dependent drinkers, lifetime alcohol intake

was unrelated to focal gray or white matter. Current alcohol intake

was unrelated to gray and white matter in both males and females.

By reducing the influence of confounding genetic factors such as a

personal or family history of alcohol dependence and chronic

E.A. de Bruin et al. / NeuroImage 26 (2005) 536–545 543

medical conditions as much as possible, the present results may

selectively relate to the effects of alcohol intake on focal gray and

white matter.

Acknowledgments

The authors are grateful to William Verheul and Hanneke

Palmen for their assistance with subject recruitment, and to Neeltje

van Haren for data flow management.

This study was supported by the Dutch Foundation for

Scientific Research (NWO/ZonMW grant 960-40000-39.

References

Abramson, J.L., Williams, S.A., Krumholz, H.M., Vaccarino, V., 2001.

Moderate alcohol consumption and risk of heart failure among older

persons. JAMA 285, 1971–1977.

Agartz, I., Momenan, R., Rawlings, R.R., Kerich, M.J., Hommer, D.W.,

1999. Hippocampal volume in patients with alcohol dependence. Arch.

Gen. Psychiatry 56, 356–363.

Agartz, I., Brag, S., Franck, J., Hammarberg, A., Okugawa, G., Svinhufvud,

K., Bergman, H., 2003. MR volumetry during acute alcohol withdrawal

and abstinence: a descriptive study. Alcohol Alcohol. 38, 71–78.

Ashburner, J., Friston, K.J., 2000. Voxel-based morphometry: the methods.

NeuroImage 11, 805–821.

Ashburner, J., Friston, K.J., 2001. Why voxel-based morphometry should

be used. NeuroImage 14, 1238–1243.

Baas, J.M., Kenemans, J.L., Mangun, G.R., 2002. Selective attention to

spatial frequency: an ERP and source localization analysis. Clin.

Neurophysiol. 113, 1840–1854.

Berger, K., Ajani, U.A., Kase, C.S., Gaziano, J.M., Buring, J.E., Glynn,

R.J., Hennekens, C.H., 1999. Light-to-moderate alcohol consumption

and risk of stroke among U.S. male physicians. N. Engl. J. Med. 341,

1557–1564.

Bookstein, F.L., 2001. ‘‘Voxel-based morphometry’’ should not be used

with imperfectly registered images. NeuroImage 14, 1454–1462.

Britton, A., Singh-Manoux, A., Marmot, M., 2004. Alcohol consumption

and cognitive function in the Whitehall II study. Am. J. Epidemiol. 160,

240–247.

Cavada, C., Goldman-Rakic, P.S., 1989. Posterior parietal cortex in rhesus

monkey: II. Evidence for segregated corticocortical networks linking

sensory and limbic areas with the frontal lobe. J. Comp. Neurol. 287,

422–445.

Chen, R., Cohen, L.G., Hallett, M., 2002. Nervous system reorganization

following injury. Neuroscience 111, 761–773.

Collins, D.L., Holmes, C.J., Peters, T.M., Evans, A.C., 1995. Automatic 3-

D model-based neuroanatomical segmentation. Hum. Brain Mapp. 3,

190–208.

Corbetta, M., Shulman, G.L., 2002. Control of goal-directed and stimulus-

driven attention in the brain. Nat. Rev., Neurosci. 3, 201–215.

Corrao, G., Bagnardi, V., Zambon, A., La Vecchia, C., 2004. A meta-

analysis of alcohol consumption and the risk of 15 diseases. Prev. Med.

38, 613–619.

Dao-Castellana, M.H., Samson, Y., Legault, F., Martinot, J.L., Aubin, H.J.,

Crouzel, C., Feldman, L., Barrucand, D., Rancurel, G., Feline, A.,

Syrota, A., 1998. Frontal dysfunction in neurologically normal chronic

alcoholic subjects: metabolic and neuropsychological findings. Psychol.

Med. 28, 1039–1048.

Davatzikos, C., 2004. Why voxel-based morphometric analysis should be

used with great caution when characterizing group differences. Neuro-

Image 23, 17–20.

De Bruin, E.A., Hulshoff Pol, H.E., Bijl, S., Schnack, H.G., Fluitman, S.,

BPcker, K.B.E., Kenemans, J.L., Kahn, R.S., Verbaten, M.N., in press.

Associations between alcohol intake and brain volumes in male and

female moderate drinkers. Alcohol. Clin. Exp. Res.

Ding, J., Eigenbrodt, M.L., Mosley Jr., T.H., Hutchinson, R.G., Folsom,

A.R., Harris, T.B., Nieto, F.J., 2004. Alcohol intake and cerebral

abnormalities on magnetic resonance imaging in a community-based

population of middle-aged adults: the Atherosclerosis Risk in Com-

munities (ARIC) study. Stroke 35, 16–21.

Djouss2, L., Ellison, R.C., Beiser, A., Scaramucci, A., D’Agostino, R.B.,

Wolf, P.A., 2002. Alcohol consumption and risk of ischemic stroke: the

Framingham Study. Stroke 33, 907–912.

Dufouil, C., Ducimetiere, P., Alperovitch, A., 1997. Sex differences in the

association between alcohol consumption and cognitive performance.

Am. J. Epidemiol. 146, 405–412.

Dufouil, C., Kersaint-Gilly, A., Besancon, V., Levy, C., Auffray, E.,

Brunnereau, L., Alperovitch, A., Tzourio, C., 2001. Longitudinal study

of blood pressure and white matter hyperintensities: the EVA MRI

cohort. Neurology 56, 921–926.

Elias, P.K., Elias, M.F., D’Agostino, R.B., Silbershatz, H., Wolf, P.A., 1999.

Alcohol consumption and cognitive performance in the Framingham

Heart Study. Am. J. Epidemiol. 150, 580–589.

Enoch, M.A., 2003. Pharmacogenomics of alcohol response and addiction.

Am. J. Pharmacogenomics 3, 217–232.

Farren, C.K., Tipton, K.F., 1999. Trait markers for alcoholism: clinical

utility. Alcohol Alcohol. 34, 649–665.

Fein, G., Di Sclafani, V., Cardenas, V.A., Goldmann, H., Tolou-Shams,

M., Meyerhoff, D.J., 2002. Cortical gray matter loss in treatment-

naive alcohol dependent individuals. Alcohol.: Clin. Exp. Res. 26,

558–564.

Fink, G.R., Frackowiak, R.S., Pietrzyk, U., Passingham, R.E., 1997.

Multiple nonprimary motor areas in the human cortex. J. Neurophysiol.

77, 2164–2174.

Ford, J.M., Sullivan, E.V., Marsh, L., White, P.M., Lim, K.O., Pfefferbaum,

A., 1994. The relationship between P300 amplitude and regional gray

matter volumes depends upon the attentional system engaged. Electro-

encephalogr. Clin. Neurophysiol. 90, 214–228.

Friston, K.J., Ashburner, J., 2004. Generative and recognition models for

neuroanatomy. NeuroImage 23, 21–24.

Graham, K., Wilsnack, R., Dawson, D., Vogeltanz, N., 1998. Should

alcohol consumption measures be adjusted for gender differences?

Addiction 93, 1137–1147.

Green, C.A., Perrin, N.A., Polen, M.R., 2004. Gender differences in the

relationships between multiple measures of alcohol consumption and

physical and mental health. Alcohol.: Clin. Exp. Res. 28, 754–764.

Hansson, E., RPnnb.ck, L., 2003. Glial neuronal signaling in the central

nervous system. FASEB J. 17, 341–348.

Harper, C., Kril, J., Daly, J., 1987. Are we drinking our neurones away?

BMJ (Clin. Res. Ed.) 294, 534–536.

Harper, C., Dixon, G., Sheedy, D., Garrick, T., 2003. Neuropathological

alterations in alcoholic brains: studies arising from the New South

Wales Tissue Resource Centre. Prog. Neuro-Psychopharmacol. Biol.

Psychiatry 27, 951–961.

Hays, W.L., 1981. Statistics, third ed. CBS College Publishing, New York,

USA.

Hommer, D.W., 2003. Male and female sensitivity to alcohol-induced brain

damage. Alcohol Res. Health 27, 181–185.

Hommer, D., Momenan, R., Rawlings, R., Ragan, P., Williams, W., Rio, D.,

Eckardt, M., 1996. Decreased corpus callosum size among alcoholic

women. Arch. Neurol. 53, 359–363.

Hommer, D.W., Momenan, R., Kaiser, E., Rawlings, R.R., 2001. Evidence

for a gender-related effect of alcoholism on brain volumes. Am. J.

Psychiatry 158, 198–204.

Hulshoff Pol, H.E., Schnack, H.G., Mandl, R.C., van Haren, N.E.,

Koning, H., Collins, D.L., Evans, A.C., Kahn, R.S., 2001. Focal gray

matter density changes in schizophrenia. Arch. Gen. Psychiatry 58,

1118–1125.

Hulshoff Pol, H.E., Schnack, H.G., Mandl, R.C., Cahn, W., Collins, D.L.,

Evans, A.C., Kahn, R.S., 2004. Focal white matter density changes in

E.A. de Bruin et al. / NeuroImage 26 (2005) 536–545544

schizophrenia: reduced inter-hemispheric connectivity. NeuroImage 21,

27–35.

Jernigan, T.L., Butters, N., DiTraglia, G., Schafer, K., Smith, T., Irwin, M.,

Grant, I., Schuckit, M., Cermak, L.S., 1991. Reduced cerebral grey

matter observed in alcoholics using magnetic resonance imaging.

Alcohol.: Clin. Exp. Res. 15, 418–427.

Johnson-Greene, D., Adams, K.M., Gilman, S., Koeppe, R.A., Junck, L.,

Kluin, K.J., Martorello, S., Heumann, M., 1997. Effects of abstinence

and relapse upon neuropsychological function and cerebral glucose

metabolism in severe chronic alcoholism. J. Clin. Neuropsychol. 19,

378–385.

Kalmijn, S., van Boxtel, M.P., Verschuren, M.W., Jolles, J., Launer, L.J.,

2002. Cigarette smoking and alcohol consumption in relation to

cognitive performance in middle age. Am. J. Epidemiol. 156, 936–944.

Kenemans, J.L., Kok, A., Smulders, F.T., 1993. Event-related potentials to

conjunctions of spatial frequency and orientation as a function of

stimulus parameters and response requirements. Electroencephalogr.

Clin. Neurophysiol. 88, 51–63.

Kenemans, J.L., Smulders, F.T., Kok, A., 1995. Selective processing of

two-dimensional visual stimuli in young and old subjects: electro-

physiological analysis. Psychophysiology 32, 108–120.

Kenemans, J.L., Baas, J.M., Mangun, G.R., Lijffijt, M., Verbaten, M.N.,

2000. On the processing of spatial frequencies as revealed by evoked-

potential source modeling. Clin. Neurophysiol. 111, 1113–1123.

Kenemans, J.L., Lijffijt, M., Camfferman, G., Verbaten, M.N., 2002. Split-

second sequential selective activation in human secondary visual cortex.

J. Cogn. Neurosci. 14, 48–61.

Kok, A., Zeef, E.J., Kenemans, J.L., Sonke, C., 1995. Age-related

decline in visual selective attention functioning: evidence derived

from ERP paradigms. Electroencephalogr. Clin. Neurophysiol., Suppl.

44, 347–357.

Kril, J.J., Halliday, G.M., Svoboda, M.D., Cartwright, H., 1997. The

cerebral cortex is damaged in chronic alcoholics. Neuroscience 79,

983–998.

Kubota, M., Nakazaki, S., Hirai, S., Saeki, N., Yamaura, A., Kusaka, T.,

2001. Alcohol consumption and frontal lobe shrinkage: study of 1432

non-alcoholic subjects. J. Neurol., Neurosurg. Psychiatry 71, 104–106.

Lawton-Craddock, A., Nixon, S.J., Tivis, R., 2003. Cognitive efficiency in

stimulant abusers with and without alcohol dependence. Alcohol.: Clin.

Exp. Res. 27, 457–464.

Lemmens, P.H., Volovics, L., De Haan, Y., 1997. Measurement of lifetime

exposure to alcohol: data quality of a self-administered questionnaire

and impact on risk assessment. Contemp. Drug Probl. 24, 581–600.

Lingford-Hughes, A.R., Acton, P.D., Gacinovic, S., Suckling, J., Busatto,

G.F., Boddington, S.J., Bullmore, E., Woodruff, P.W., Costa, D.C.,

Pilowsky, L.S., Ell, P.J., Marshall, E.J., Kerwin, R.W., 1998. Reduced

levels of GABA-benzodiazepine receptor in alcohol dependency in the

absence of grey matter atrophy. Br. J. Psychiatry 173, 116–122.

Longnecker, M.P., 1995. Alcohol consumption and risk of cancer in

humans: an overview. Alcohol 12, 87–96.

Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P., 1997.

Multimodality image registration by maximization of mutual informa-

tion. IEEE Trans. Med. Imaging 16, 187–198.

Meyerhoff, D.J., Blumenfeld, R., Truran, D., Lindgren, J., Flenniken, D.,

Cardenas, V., Chao, L.L., Rothlind, J., Studholme, C., Weiner, M.W.,

2004. Effects of heavy drinking, binge drinking, and family history of

alcoholism on regional brain metabolites. Alcohol.: Clin. Exp. Res. 28,

650–661.

Mukamal, K.J., Longstreth Jr., W.T., Mittleman, M.A., Crum, R.M.,

Siscovick, D.S., 2001. Alcohol consumption and subclinical findings on

magnetic resonance imaging of the brain in older adults: the

Cardiovascular Health Study. Stroke 32, 1939–1946.

Mumenthaler, M.S., Taylor, J.L., O’Hara, R., Yesavage, J.A., 1999.

Gender differences in moderate drinking effects. Alcohol Res. Health

23, 55–64.

Parks, M.H., Dawant, B.M., Riddle, W.R., Hartmann, S.L., Dietrich, M.S.,

Nickel, M.K., Price, R.R., Martin, P.R., 2002. Longitudinal brain

metabolic characterization of chronic alcoholics with proton magnetic

resonance spectroscopy. Alcohol.: Clin. Exp. Res. 26, 1368–1380.

Parsons, O.A., 1994. Neuropsychological measures and event-related

potentials in alcoholics: interrelationships, long-term reliabilities, and

prediction of resumption of drinking. J. Clin. Psychol. 50, 37–46.

Parsons, O.A., 1998. Neurocognitive deficits in alcoholics and social

drinkers: a continuum? Alcohol.: Clin. Exp. Res. 22, 954–961.

Parsons, O.A., Nixon, S.J., 1998. Cognitive functioning in sober social

drinkers: a review of the research since 1986. J. Stud. Alcohol 59,

180–190.

Pfefferbaum, A., Lim, K.O., Zipursky, R.B., Mathalon, D.H., Rosenbloom,

M.J., Lane, B., Ha, C.N., Sullivan, E.V., 1992. Brain gray and white

matter volume loss accelerates with aging in chronic alcoholics: a

quantitative MRI study. Alcohol.: Clin. Exp. Res. 16, 1078–1089.

Pfefferbaum, A., Sullivan, E.V., Rosenbloom, M.J., Shear, P.K., Mathalon,

D.H., Lim, K.O., 1993. Increase in brain cerebrospinal fluid volume is

greater in older than in younger alcoholic patients: a replication study

and CT/MRI comparison. Psychiatry Res. 50, 257–274.

Pfefferbaum, A., Sullivan, E.V., Mathalon, D.H., Shear, P.K., Rosenbloom,

M.J., Lim, K.O., 1995. Longitudinal changes in magnetic resonance

imaging brain volumes in abstinent and relapsed alcoholics. Alcohol.:

Clin. Exp. Res. 19, 1177–1191.

Pfefferbaum, A., Sullivan, E.V., Mathalon, D.H., Lim, K.O., 1997. Frontal

lobe volume loss observed with magnetic resonance imaging in older

chronic alcoholics. Alcohol.: Clin. Exp. Res. 21, 521–529.

Pfefferbaum, A., Desmond, J.E., Galloway, C., Menon, V., Glover, G.H.,

Sullivan, E.V., 2001a. Reorganization of frontal systems used by

alcoholics for spatial working memory: an fMRI study. NeuroImage 14,

7–20.

Pfefferbaum, A., Rosenbloom, M., Deshmukh, A., Sullivan, E.V., 2001b.

Sex differences in the effects of alcohol on brain structure. Am. J.

Psychiatry 158, 188–197.

Porjesz, B., Begeleiter, H., 1996. Effects of alcohol on electrophysiological

activity of the brain. In: Begleiter, H., Kissin, B. (Eds.), The

Pharmacology of Alcohol and Alcohol Dependence, 1st edR Oxford

Univ. Press, New York, pp. 207–247.

Quertemont, E., 2004. Genetic polymorphism in ethanol metabolism:

acetaldehyde contribution to alcohol abuse and alcoholism. Mol.

Psychiatry 9, 570–581.

Robins, L.N., Wing, J., Wittchen, H.U., Helzer, J.E., Babor, T.F., Burke, J.,

Farmer, A., Jablenski, A., Pickens, R., Regier, D.A., 1988. The

composite international diagnostic interview: an epidemiologic instru-

ment suitable for use in conjunction with different diagnostic systems

and in different cultures. Arch. Gen. Psychiatry 45, 1069–1077.

Sacco, R.L., Elkind, M., Boden-Albala, B., Lin, I.F., Kargman, D.E.,

Hauser, W.A., Shea, S., Paik, M.C., 1999. The protective effect of

moderate alcohol consumption on ischemic stroke. JAMA 281, 53–60.

Schmand, B., Lindeboom, J., Van Harskamp, F., 2003. NLV: Neder-

landse Leestest voor volwassenen. Zwets and Zeitlinger, Lisse, The

Netherlands.

Schnack, H.G., Hulshoff Pol, H.E., Baar2, W.F.C., Staal, W.G., Viergever,

M.A., Kahn, R.S., 2001. Automated separation of gray and white matter

from MR images of the human brain. NeuroImage 13, 230–237.

Schroth, G., Naegele, T., Klose, U., Mann, K., Petersen, D., 1988.

Reversible brain shrinkage in abstinent alcoholics, measured by MRI.

Neuroradiology 30, 385–389.

Schuckit, M.A., 2000. Genetics of the risk for alcoholism. Am. J. Addict. 9,

103–112.

Schweinsburg, B.C., Taylor, M.J., Alhassoon, O.M., Videen, J.S., Brown,

G.G., Patterson, T.L., Berger, F., Grant, I., 2001. Chemical pathology in

brain white matter of recently detoxified alcoholics: a 1 H magnetic

resonance spectroscopy investigation of alcohol-associated frontal lobe

injury. Alcohol.: Clin. Exp. Res. 25, 924–934.

Shear, P.K., Jernigan, T.L., Butters, N., 1994. Volumetric magnetic

resonance imaging quantification of longitudinal brain changes in

abstinent alcoholics. Alcohol.: Clin. Exp. Res. 18, 172–176.

Shy, H., Malaiyandi, L., Timiras, P.S., 2000. Protective action of 17b-

E.A. de Bruin et al. / NeuroImage 26 (2005) 536–545 545

estradiol and tamoxifen on glutamate toxicity in glial cells. Int. J. Dev.

Neurosci. 18, 289–297.

Singer, C.A., Rogers, K.L., Strickland, T.M., Dorsa, D.M., 1996. Estrogen

protects primary cortical neurons from glutamate toxicity. Neurosci.

Lett. 212, 13–16.

Skinner, H.A., Sheu, W.J., 1982. Reliability of alcohol use indices.

The lifetime drinking history and the MAST. J. Stud. Alcohol 43,

1157–1170.

Sled, J.G., Zijdenbos, A.P., Evans, A.C., 1998. A nonparametric method for

automatic correction of intensity nonuniformity in MRI data. IEEE

Trans. Med. Imaging 17, 87–97.

Suter, P.M., Vetter, W., 1999. Alcohol and ischemic stroke. Nutr. Rev. 57,

310–314.

Taki, Y., Goto, R., Evans, A., Zijdenbos, A., Neelin, P., Lerch, J., Sato, K.,

Ono, S., Kinomura, S., Nakagawa, M., Sugiura, M., Watanabe, J.,

Kawashima, R., Fukuda, H., 2004. Voxel-based morphometry of human

brain with age and cerebrovascular risk factors. Neurobiol. Aging 25,

455–463.

Tapert, S.F., Brown, G.G., Kindermann, S.S., Cheung, E.H., Frank, L.R.,

Brown, S.A., 2001. fMRI measurement of brain dysfunction in alcohol-

dependent young women. Alcohol.: Clin. Exp. Res. 25, 236–245.

Tsukiura, T., Fujii, T., Takahashi, T., Xiao, R., Inase, M., Iijima, T.,

Yamadori, A., Okuda, J., 2001. Neuroanatomical discrimination

between manipulating and maintaining processes involved in verbal

working memory: a functional MRI study. Cogn. Brain Res. 11, 13–21.

Wang, G.J., Volkow, N.D., Fowler, J.S., Franceschi, D., Wong, C.T.,

Pappas, N.R., Netusil, N., Zhu, W., Felder, C., Ma, Y., 2003. Alcohol

intoxication induces greater reductions in brain metabolism in male than

in female subjects. Alcohol.: Clin. Exp. Res. 27, 909–917.

Worsley, K.J., Marrett, S., Neelin, P., Vandal, A.C., Friston, K.J.,

Evans, A.C., 1996. A unified statistical approach for determining

significant signals in images of cerebral activation. Hum. Brain

Mapp. 4, 58–73.

Zipursky, R.B., Lim, K.C., Pfefferbaum, A., 1989. MRI study of brain

changes with short-term abstinence from alcohol. Alcohol.: Clin. Exp.

Res. 13, 664–666.