BRAINA JOURNAL OF NEUROLOGY

Cerebral white matter recovery in abstinentalcoholics—a multimodality magneticresonance studyStefan Gazdzinski,1 Timothy C. Durazzo,1,2 Anderson Mon,2 Ping-Hong Yeh1 andDieter J. Meyerhoff1,2

1 Centre for Imaging of Neurodegenerative Diseases, Veterans Administration Medical Centre, San Francisco, USA

2 Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA

Correspondence to: Stefan Gazdzinski,

Jagiellonian University,

M. Smoluchowski Institute of Physics,

Reymonta 4,

30-059 Krakow,

Poland

E-mail: stefan.gazdzinski@uj.edu.pl

Most previous neuroimaging studies of alcohol-induced brain injury and recovery thereof during abstinence from alcohol used a

single imaging modality. They have demonstrated widespread microstructural, macrostructural or metabolite abnormalities that

were partially reversible with abstinence, with the cigarette smoking potentially modulating these processes. The goals of this

study were to evaluate white matter injury and recovery thereof, simultaneously with diffusion tensor imaging, magnetic

resonance imaging and spectroscopy in the same cohort; and to evaluate the relationships between outcome measures of

similar regions. We scanned 16 non-smoking and 20 smoking alcohol-dependent individuals at 1 week of abstinence from

alcohol and 22 non-smoking light drinkers using a 1.5 T magnetic resonance scanner. Ten non-smoking alcohol-dependent

individuals and 11 smoking alcohol-dependent individuals were re-scanned at 1 month of abstinence. All regional diffusion

tensor imaging, magnetic resonance imaging and spectroscopic outcome measures were calculated over comparable volumes of

frontal, temporal, parietal and occipital white matter. At 1 week of abstinence and relative to non-smoking light drinkers,

non-smoking alcohol-dependent individuals had higher mean diffusivity in frontal, temporal and parietal white matter (all

P50.008), whereas smoking alcohol-dependent individuals had elevated mean diffusivity only in frontal white matter

(P = 0.03). Smoking alcohol-dependent individuals demonstrated lower concentrations of N-acetyl-aspartate (a marker of neu-

ronal viability) in frontal white matter (P = 0.03), whereas non-smoking alcohol-dependent individuals had lower

N-acetyl-aspartate in parietal white matter (P = 0.05). These abnormalities were not accompanied by detectable white matter

atrophy. However, the patterns of white matter recovery were different between non-smoking alcohol-dependent individuals and

smoking alcohol-dependent individuals. In non-smoking alcohol-dependent individuals, the increase in fractional anisotropy of

temporal white matter (P = 0.003) was accompanied by a pattern of decreases mean diffusivity in all regions over 1 month of

abstinence; no corresponding changes were observed in smoking alcohol-dependent individuals. In contrast, a pattern of white

matter volume increase in frontal and temporal lobes was apparent in smoking alcohol-dependent individuals but not in

non-smoking alcohol-dependent individuals. These results were not accompanied by significant changes in metabolite concen-

trations. Finally, there were no consistent patterns of association between measures obtained with different imaging modalities,

doi:10.1093/brain/awp343 Brain 2010: 133; 1043–1053 | 1043

Received August 13, 2009. Revised October 24, 2009. Accepted December 16, 2009. Advance Access publication February 4, 2010

� The Author (2010). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved.

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either cross-sectionally or longitudinally. These data demonstrate significant white matter improvements with abstinence from

alcohol, reflected either as microstructural recovery or volumetric increases that depend on the smoking status of the partici-

pants. We believe our results to be important, as they demonstrate that use of a single imaging modality provides an incomplete

picture of neurobiological processes associated with alcohol-induced brain injury and recovery thereof that may even lead to

improper interpretation of results.

Keywords: White matter; diffusion tensor; spectroscopy; cigarette smoking; alcohol dependence

Abbreviations: BMI = body mass index; Cho = choline-containing compounds; dMANOVA = dependent measures multivariateanalysis of variance; DTI = diffusion tensor imaging; ES = effect size; FA = fractional anisotropy; 1H MRS = proton magnetic reso-nance spectroscopy; MANCOVA = multivariate analyses of co-variance; MD = mean diffusivity; NAA = N-acetyl-aspartate;nsADI = non-smoking alcohol-dependent individual; nsLD = non-smoking light drinkers; sADI = smoking alcohol-dependentindividual

IntroductionThe majority of in vivo human neuroimaging studies investigating

the recovery of alcohol-induced neurobiological injury during

abstinence from alcohol in alcohol-dependent individuals have

used a single imaging modality. Cross-sectional studies reported

morphological abnormalities in both white matter and grey matter

(for review see Sullivan, 2000). Proton magnetic resonance spec-

troscopy (1H MRS) generally demonstrated lower concentrations

of N-acetyl-aspartate (NAA, a marker of neuronal viability, which

may reflect neuronal loss, lower neuronal density, atrophied den-

drites and axons, and/or deranged neuronal metabolism) and

choline-containing compounds (Cho; involved in membrane turn-

over/synthesis; see Ross and Bluml, 2001), most notably in the

frontal lobes, medial temporal lobes and cerebellum (Bendszus

et al., 2001; Parks et al., 2002; Gazdzinski et al., 2008a).

Cross-sectional diffusion tensor imaging (DTI) studies in

alcohol-dependent individuals have indicated decreased fractional

anisotropy (FA) and increased mean diffusivity (MD) in the genu,

body and splenium of the corpus callosum, as well as in the cen-

trum semiovale, which suggest compromised axonal/myelin integ-

rity (e.g. Pfefferbaum et al., 2005a). FA quantifies the

directionality of water diffusion and is an indicator of white

matter coherence and white matter organization within fibre

tracts. FA reductions are attributed to degradation of myelin

sheaths and axonal membranes (Pierpaoli et al., 2001), abnorm-

alities of myelin with sparing of the axonal fibres (e.g. Song et al.,

2005) or reduced density of axonal fibres (Takahashi et al., 2002).

MD quantifies the average magnitude of microscopic water diffu-

sion, which is likely to reflect cellular density and extracellular fluid

volume (Sotak, 2004), and relates to the volume fraction of the

interstitial space (Sotak, 2004; Song et al., 2005).

Longitudinal MRI studies of alcohol-dependent individuals

during abstinence from alcohol report increases in white matter

volume (Shear et al., 1994; Agartz et al., 2003), though one study

did not find any significant change in white matter volume

(Pfefferbaum et al., 1995). Longitudinal MRS studies of

alcohol-dependent individuals demonstrated regional increases in

concentrations of NAA and Cho in cerebral white matter and cer-

ebellum (Bendszus et al., 2001; Parks et al., 2002; Ende et al.,

2005; Durazzo et al., 2006; Bartsch et al., 2007). However, to

date, there are no longitudinal DTI studies with abstinent

alcohol-dependent individuals. Furthermore, it is not clear to

what extent the results obtained by different imaging modalities

are interrelated and yield complementary findings or whether var-

ious imaging modalities yield results that are unique and

non-convergent with other modalities.

Up to now, only a few studies have reported interrelations

between results obtained with different imaging modalities

within the same cohort of alcohol-dependent individuals.

Pfefferbaum and colleagues (2005a) reported that volume reduc-

tion in the corpus callosum of alcohol-dependent individuals was

accompanied by FA and MD abnormalities, suggesting disruption

in the structural constituents of local white matter. Bartsch et al.

(2007) demonstrated that global white matter volume increases

correlated with cerebellar and fronto-mesial Cho increases in

non-smoking alcoholics over 6–7 weeks of abstinence from alco-

hol. Our own data suggest that white matter regions of lower FA

spatially coincide with regions of NAA abnormalities in alcoholics

(Wang et al., 2009).

Additionally, co-morbid chronic cigarette smoking, which occurs

in �60–80% of alcohol-dependent individuals (Romberger and

Grant, 2004), is associated with greater cross-sectional NAA and

Cho abnormalities in multiple brain regions of alcohol-dependent

individuals (Durazzo et al., 2004), as well as diminished regional

brain metabolite and neurocognitive recovery with abstinence

from alcohol (Durazzo et al., 2006, 2007b; Gazdzinski et al.,

2008a).

The goals of this study were: (i) to measure white matter vol-

umes, MD, FA and concentrations of major 1H MRS detectable

metabolites from comparable white matter regions in the same

cohort of alcohol-dependent individuals at 1 week of abstinence

from alcohol; and (ii) to assess changes in these measures over

1 month of abstinence from alcohol. We tested the following

hypotheses: (i) at baseline, soon after last alcohol consumption,

smoking alcohol-dependent individuals (sADIs) demonstrate the

highest MD and lowest FA, followed by non-smoking

alcohol-dependent individuals (nsADIs) and non-smoking light

drinkers (nsLD) in all major lobes; (ii) over 1 month of abstinence

from alcohol, nsADIs have greater increases in regional white

matter FA, volumes and concentrations of NAA and Cho than

sADIs, as well as greater decreases of regional white matter

1044 | Brain 2010: 133; 1043–1053 S. Gazdzinski et al.

MD; (iii) at baseline, higher MD is associated with lower FA,

smaller white matter volumes and lower concentrations of NAA;

and finally (iv) over 1 month of abstinence, decreasing lobar white

matter MD is associated with increases of FA, white matter vol-

umes and NAA concentrations, and increasing Cho is associated

with increasing white matter volumes.

Material and methods

ParticipantsSixteen nsADIs (51.5� 10.3 years, two female) and 20 sADIs

(47.6�9.5 years, one female) were part of a longitudinal study that

assessed the co-morbid effects of alcohol dependence and chronic

cigarette smoking on neurobiological and neurocognitive abnormalities

in abstinent alcoholics (Durazzo et al., 2007a). The number of partic-

ipants available for this analysis was limited by the availability of DTI

data. nsADIs and sADIs were primarily recruited from the San

Francisco Veterans Affairs Medical Centre Substance Abuse Day

Hospital and secondarily from the San Francisco Kaiser Permanente

Chemical Dependence Recovery Program. They were first studied at

baseline 4.6� 2.6 and 4.5� 2.1 days after their last alcoholic drink,

respectively. Ten nsADIs and 11 sADIs were re-scanned after

37.5� 9.6 and 31.2� 6.9 days of abstinence, respectively (P = 0.10).

Twenty-two healthy, age-matched nsLDs (48.3� 8.4 years, two

females) were recruited from the San Francisco Bay Area community

and nine were re-scanned after �1 year. Only 8 of the 36

alcohol-dependent individuals from this study contributed to our pre-

vious reports.

The inclusion and exclusion criteria are fully described in Durazzo

et al. (2004). In short, all alcohol-dependent individuals met the

Diagnostic and Statistical Manual of Mental Disorders (fourth edition)

criteria for alcohol dependence with physiological dependence and

consumed more than 150 standard alcoholic drinks per month (80

for females) for at least 8 years prior to enrolment into the study. A

standard drink contains 13.6 g of pure ethanol, equivalent of 12 oz

beer, 5 oz wine or 1.5 oz liquor. All participants were free of general

medical, neurological and psychiatric conditions, except unipolar mood

disorders, hypertension (medication controlled) and hepatitis C in

alcohol-dependent individuals. These co-morbidities were not exclu-

sionary in alcohol-dependent individuals, due to their high prevalence

among alcohol-dependent individuals (Hasin et al., 2007) and chronic

cigarette smokers (Fergusson et al., 2003). Six sADIs and two nsADIs

met the Diagnostic and Statistical Manual of Mental Disorders (fourth

edition) criteria for substance-induced (alcohol) mood disorder with

depressive features. Three sADIs and two nsADIs were diagnosed

with recurrent major depression, whereas two nsADIs were diagnosed

with major depression, which were in partial or sustained full remis-

sion. One sADI had alcohol-induced psychotic disorder with hallucina-

tions. Two nsADIs met criteria for past cannabis abuse in sustained full

remission and their last use was 1 and 2 years before enrolment,

whereas one sADI met criteria for past cocaine dependence and was

in sustained full remission with last use 8 years before enrolment.

One sADI and one nsADI with a unipolar mood disorder were

taking fluoxetine. Six nsADIs and nine sADIs had medically controlled

hypertension.

Alcohol consumption and smoking behaviour over lifetime were

assessed via the Lifetime Drinking History (Skinner and Sheu, 1982;

Sobell et al., 1988; Sobell and Sobell, 1992) and the Fagerstrom

Tolerance Test for Nicotine Dependence (Fagerstrom et al., 1991),

respectively. All sADIs continued to smoke at their baseline levels

over the assessment interval, except for one individual, who stopped

smoking and used a nicotine patch; all his magnetic resonance mea-

sures improved after baseline, but were within the range of the sADIs.

Eight nsADIs never smoked during their lifetime and eight quit smok-

ing between 2 and 30 years prior to enrolment. The DTI indices, vol-

umes and metabolite concentrations were not statistically different

between those who never smoked and former smokers. Of the 21

alcohol-dependent individuals, 19 who had a follow-up scan partici-

pated in continued out-patient substance abuse treatment programs at

the San Francisco Veteran Affairs Medical Centre for the study dura-

tion and were randomly tested for alcohol consumption and given

weekly drug screens to ensure abstinence.

Clinical laboratory measures, obtained within 1 day of the magnetic

resonance studies, assessed for hepatocellular injury, red blood cell

status and nutritional status (serum pre-albumin; Weinrebe et al.,

2002). Participants were allowed to smoke ad libitum prior to mag-

netic resonance scans. Alcohol withdrawal (Sullivan et al., 1989),

depressive (Beck, 1978) and anxiety symptomatology (Spielberger

et al., 1977) were assessed within 1 day of the scanning session. No

participant had clinically significant withdrawal symptoms. The

Institutional Review Boards of the University of California San

Francisco and the San Francisco Veteran Affairs Medical Centre

approved all procedures, and written informed consent was obtained

from all participants prior to study.

Data acquisitionAll magnetic resonance data were obtained on a standard 1.5 T MRI

system (Siemens Vision, Iselin, NJ, USA). DTI was performed with a

single-shot double-refocused spin-echo echo-planar imaging sequence

(repetition time/echo time = 5000/100 ms, 2.4�2.4�5 mm3, 20 con-

tiguous slices covering supratentorial white matter, 3 min scan time),

with diffusion sensitizing gradients of b = 0, 160, 360, 640 and 1000 s/

mm2 applied along six independent directions and double refocusing

diffusion gradients to remove eddy current-related geometrical image

distortions in DTI (Reese et al., 2003). 3D T1-weighted images were

acquired with a standard magnetization prepared rapid gradient echo

sequence (repetition time/echo time/inversion time = 10/7/300 ms, 15�

flip angle, 1� 1�1.5 mm3, 7 min), for segmentation. Additionally,

axial-oblique double spin-echo (repetition time/echo time1/echo

time2 = 2500/20/80 ms, 1� 1�3 mm3, 12 min) proton density and

T2-weighted images were acquired and used to assess white matter

signal hyperintensity qualitatively. Finally, metabolite spectra were

acquired with proton multislice short-echo time 1H MRS imaging (rep-

etition time/echo time/inversion time = 1800/25/165 ms, 30 min) in

three parallel, oblique-axial slices, each 15 mm thick and 6 mm

apart and covering the major cerebral lobes (Fig. 1). All data were

carefully reviewed; motion artefacts and overall poor quality were

exclusionary.

Data processingAs the lack of full brain coverage of our MRS imaging sequence pre-

cluded voxel-wise analyses, we compared measures obtained with

these modalities within comparable white matter regions.

Probability maps of grey matter, white matter and CSF in frontal,

parietal, temporal and occipital white matter were obtained by com-

bining Expectation–Maximization Segmentation (Van Leemput et al.,

1999) with an atlas-based deformable registration method that was

used to identify regions of interest in the brain automatically, as pre-

viously described (Cardenas et al., 2005). These maps were also used

White matter recovery in alcoholics Brain 2010: 133; 1043–1053 | 1045

to calculate white matter volumes. To account for individual variation

in head size, absolute volumes of identified structures were divided by

intracranial volume, defined as the sum of white matter, grey matter

and CSF.

For the DTI analyses, MD and FA were calculated in every voxel

using a simple least squares fit of the tensor model using all five

b-values. We aligned and interpolated the 3D T1-weighted images

and corresponding lobar white matter probability maps to correspond-

ing (b0) diffusion scans. Regions affected by susceptibility artefact

(mostly orbito-frontal white matter) were manually removed from

FA and MD images. The spatial extents of analysed DTI voxels

included frontal, parietal, temporal and occipital lobes, and were com-

parable across participants and virtually the same in the longitudinal

scans.

To minimize the effects of partial volumes, small mis-registrations

and white matter hyperintensities (that generally segment as grey

matter or CSF) on the analyses, we used only voxels containing

495% of white matter and with FA40.2. Thus, this study effectively

compares normal appearing white matter between groups. Then, we

calculated median MD and median FA within similar white matter

regions as used for structural and spectroscopic analyses (similar to

Lim et al., 1999; Fig. 1). Arithmetic means were not calculated, as

the distributions of MD and FA were not Gaussian. The lobar MD and

FA were calculated on �4700 voxels per individual for frontal white

matter, 1700 voxels for temporal white matter, 2100 voxels for pari-

etal white matter and 700 voxels for occipital white matter, with no

significant differences in voxel counts between groups. The voxel

counts were also similar in the smaller sample used for longitudinal

analyses and did not differ significantly between baseline and

follow-up scans. This approach was valid, as our preliminary

voxel-wise DTI analyses determined that microstructural abnormalities

in alcohol-dependent individuals, who were part of our cohort, are

relatively widespread throughout white matter in all lobes (Yeh

et al., 2008).

Processing details for spectroscopic data were described in

Meyerhoff et al. (2004). The final MRS imaging outcome measures

were tissue-specific, atrophy corrected, absolute, mean metabolite

concentrations in institutional units over similar regions as used in

DTI and structural analyses.

Study design and statistical analysesThe cross-sectional analyses evaluated for differences between groups

in median MD, median FA, with Generalized Linear Model (Wald �2),

separate for each white matter region, due to heterogeneous variances

in these measures. Lobar white matter volumes and individual meta-

bolite concentrations over frontal, temporal, parietal and occipital

white matter were compared between groups with one-way multi-

variate analyses of co-variance (MANCOVA; Wilks’ lambda), followed

by univariate analyses of co-variance and pairwise one-tailed t-tests.

Although age was not significantly different between groups, the

cohort spanned a large age range (28–66 years) and therefore age

was used as a covariate in all analyses to account for the potential

effects of ageing (e.g. Bartzokis et al., 2001; Schuff et al., 2002;

Pfefferbaum et al., 2005b). For NAA and Cho comparisons, body

mass index (BMI, calculated as body mass in kilograms divided by

height in metres squared) was used as a second covariate, because

we observed associations between BMI and metabolite concentrations

in a cohort of healthy middle-aged individuals (Gazdzinski et al.,

2008b). The BMI did not correlate with MD, FA or white matter vol-

umes (P40.20, corrected for age) in nsLDs and was not used as a

covariate in volumetric and diffusion analyses. To correct for

experiment-wise error rate, we created the following families of out-

come measures: (i) lobar white matter diffusion measures—MD, FA;

(ii) lobar white matter volumes; and (iii) lobar metabolite concentra-

tions (NAA, Cho). When MANCOVA was significant, the P-values of

the t-tests were multiplied by the number of magnetic resonance

parameters within each family. Otherwise, if the MANCOVA was

not significant, the significance levels of t-tests were adjusted by mul-

tiplying the P-values by 4 (number of evaluated white matter regions)

and by the number of magnetic resonance parameters within each

family. Finally, to account for differences in drinking history between

nsADIs and sADIs, these groups were compared with additional

covariates (age of onset of heavy drinking, total lifetime consump-

tion of ethanol and average number of drinks per month over life-

time) in follow-up analyses. Cohen’s d was used to estimate effect

sizes (ES).

Longitudinal analyses comparing longitudinal changes between

nsADIs and sADIs utilized doubly multivariate analysis of repeated

dependent measures multivariate analysis of variance (dMANOVA;

Tabachnick and Fidell, 2001), separately for each imaging parameter.

This procedure allows for the simultaneous evaluation of change in

multiple dependent measures obtained on different occasions (i.e.

changes in FA and MD), while protecting against the increased

family-wise error rate that occurs when separately evaluating multiple

dependent measures (the conventional approach). Additionally, to

account for individual differences in scan intervals, we calculated

monthly rates of change as:

Monthly rate of change

¼Measure ðfollow� upÞ �Measure ðbaselineÞ

Measure ðbaselineÞ � ðTime between scans in monthsÞ� 100%,

and compared change rates between nsADIs and sADIs with direc-

tional independent t-tests. Pearson’s correlations evaluated relation-

ships between outcome measures and their longitudinal changes

with estimates of drinking and smoking severity. Linear regression

analyses were used to ensure that cigarette smoking was not a signif-

icant factor modulating these correlations. The associations among FA,

MD, volumes and metabolite concentrations were considered separate

Figure 1 Brain regions scanned with our magnetic resonance

spectroscopic sequence. Mean absolute atrophy-corrected

metabolite concentrations were calculated over anatomically

defined white matter lobar regions. The DTI and MRI sequences

covered basically all lobar white matter.

1046 | Brain 2010: 133; 1043–1053 S. Gazdzinski et al.

scientific questions and no corrections were made for the number of

magnetic resonance outcome measures. To correct the significance

levels for multiplicity, we used the same families as described above.

Additionally, the significance levels for correlation analyses within the

same white matter region were corrected for experiment-wise error

rate by multiplying the P-values by 4 (number of evaluated regions),

whereas the P-values were multiplied by 16 when the evaluated mea-

sures were obtained from different regions. For example, the signifi-

cance level of correlation between frontal white matter volume and

frontal white matter FA was multiplied by 8 (four regions, two mea-

sures in the family of diffusion measures), whereas the significance

level for correlation between frontal white matter NAA and parietal

white matter FA was multiplied by 64 (16�2�2). Corrected P50.05

was considered statistically significant. All statistical tests were con-

ducted with the Statistical Package for the Social Sciences-16.0 for

Windows (SPSS; Chicago, IL, USA).

Results

Participant characterizationTable 1 lists alcohol consumption measures and other demo-

graphic and clinical variables. All groups were equivalent on age

[F(2,55) = 0.83, P = 0.44] but not on education [F(2,55) = 12.1,

P = 0.001], with nsLDs having more education than both nsADIs

and sADIs (both P50.003). nsADIs and sADIs consumed a similar

average number of alcoholic drinks per month over 1 and 3 years

prior to enrolment (P40.48). However, nsADIs began drinking at

heavy levels (i.e. 4100 drinks/month) at older age (30.4� 11.7

versus 19.5� 3.5, P = 0.001) and had fewer drinks per month over

lifetime than sADIs (162� 86 versus 271� 168, P = 0.02). nsADIs

did not differ from sADIs on self-reported measures of depressive,

anxiety and withdrawal symptomatology, haemoglobin, haemato-

crit and red blood cell counts (all P40.34). In both

alcohol-dependent individual groups, the liver enzymes

�-glutamyltransferase and aspartate-aminotransferase levels were

elevated at baseline but normalized before follow-up, whereas

their red blood cell counts were below normal at both assess-

ments. Pre-albumin was within normal range for both nsADIs

and sADIs, suggesting no gross malnourishment.

The sADI Fagerstrom score was 5.2� 2.2, indicating medium to

high levels of nicotine dependence. The sADI participants smoked

on an average 20.6� 11.6 cigarettes per day for 20.8� 12.4

years, resulting in 24� 22 pack-years. Most nsADIs were over-

weight or obese (BMI425), whereas sADIs were generally at

normal weight or overweight (205BMI530; Table 1). These

group characteristics were similar in the smaller cohort used for

longitudinal analyses.

According to a clinical neuroradiologist’s review of study MRI,

five nsADIs (31%) and nine sADIs (45%) demonstrated white

matter signal hyperintensity (�2 = 0.71, P = 0.40). Specifically,

three nsADIs and two sADIs had punctate foci, two nsADIs and

four sADIs had early confluence of white matter signal hyperin-

tensities, and three sADIs (but no nsADIs) had large confluent

areas of white matter signal hyperintensities. The presence of

these white matter signal hyperintensities did not affect our DTI

results as they segmented as grey matter or CSF, and thus, did not

contribute to our white matter MD and FA measures. The smaller

sample used in longitudinal analyses had demographics, clinical

indices and white matter signal hyperintensity distribution similar

to the larger cross-sectional cohort at baseline.

Table 1 Demographics, alcohol consumption and clinical variables (mean � SD)

Cross-sectional sample Smaller longitudinal sample

Parameter nsLDn = 22 (2F)

nsADIn = 16 (2F)

sADIn = 20 (1F)

nsADIn = 10 (1F)

sADIn = 11 (0F)

Age (years) 48.3�8.4 51.5� 10.3 47.7� 9.5 50.3� 10.3 50.9� 10.9

Education (years) 17.1�2.7 14.3� 2.2 13.8� 2.0 14.8� 2.4 13.4� 1.4

AMNART – 117� 6 114� 9 119� 6 115� 8

BDI 2.8�3.4 11.1� 8.6 15.5� 8.9 7.1� 7.3 11.6� 5.3

1-year average (drinks/month) 19�17 359� 172 372� 146 375� 211 385� 153

Lifetime average (drinks/month) 17�10 162� 86� 277� 168� 166� 97 324� 206

Total lifetime consumption of ethanol (kg) – 891� 516 1459� 1032 866� 531�� 1825� 1242��

Age at onset of heavy drinking (years) – 30.4� 11.7�� 19.5� 3.4�� 30.2� 10.3�� 20.5� 2.6��

Major depressive disorder (n) 0 4 3 2 1

Substance induced mood disorder (n) 0 2 6 0 4

Hypertension (n) 0 6 9 4 5

Hepatitis-C [n] 0 0 2 0 2

GGT (institutional units) 26�17 151� 159 98� 53 100� 97 72� 17

AST (institutional units) 27�7 45� 24 51� 39 41� 20 71� 54

RBC (M/mm3) 5.02�0.33 4.38� 0.44 4.49� 0.55 4.48� 0.38 4.55� 0.71

BMI (kg/m2) 25.8�5.5 29.8� 4.7� 25.7� 6.3� 30.5� 5.2�� 23.7� 4.1��

Values are mean� SD.AMNART = American National Adult Reading Test; BDI = Beck Depression Inventory; 1-year average = number of drinks per month over 1-year prior to study; lifetimeaverage = number of drinks per month over lifetime; age at onset of heavy drinking = age when alcohol consumption exceeded 100 drinks per month;

GGT = gamma-glutamyltransferase (local normal range = 7–64 institutional units); AST = aspartate aminotransferase (local normal range = 5–35 institutional units);RBC = red blood cell count (local normal range = 4.7–6.1 M/mm3).�P50.05; ��P50.01.

White matter recovery in alcoholics Brain 2010: 133; 1043–1053 | 1047

Cross-sectional group comparisons atbaselineThis analysis tested the first hypothesis that MD is highest and FA

is lowest in sADIs, followed by nsADIs and nsLDs.

Comparison of frontal white matter MD using Generalized

Linear Model indicated significant group differences

[�2(2) = 10.45, P = 0.03]. Follow-up group comparisons revealed

that nsADIs had 3.9% higher (P = 0.005, ES = 0.96) and sADIs

2% higher (P = 0.05, ES = 0.62) frontal white matter MD than

nsLDs, with a trend for 1.9% higher MD in nsADIs than in

sADIs (P = 0.10, ES = 0.44). Among the hypothesized group differ-

ences, nsADIs demonstrated higher MD than nsLDs in the tem-

poral (3.1%, P = 0.005, ES = 0.88) and parietal lobes

(3.3%, P = 0.003, ES = 0.96), but sADIs were not significantly dif-

ferent from nsLDs (ES50.4). No group differences were apparent

for any of the lobar FA measures (P40.20); the effect sizes were

consistently larger for comparisons between nsADIs and nsLDs

than for comparisons between sADIs and nsLDs, consistent with

MD results.

Lobar white matter volumes were insignificantly different

between the three groups (53%, P40.09, ES50.3). Among

metabolites and after using age and BMI as covariates, the

MANCOVA comparing NAA between groups was marginally sig-

nificant [F(8,80) = 1.81, P = 0.09]. However, among the hypothe-

sized contrasts, NAA was 9% lower in frontal white matter of

sADIs than in nsLDs (P = 0.03, ES = 0.85) and 10% lower in pari-

etal white matter of nsADIs as compared with nsLDs (P = 0.05,

ES = 0.87), with a trend for 8% lower NAA in parietal white

matter of sADIs relative to nsLDs (P = 0.08, ES = 0.71). Also,

lobar white matter Cho, creatine and m-inositol concentrations

did not differ significantly between groups (all P40.44, ES50.35).

Finally, all the differences between nsADIs and sADIs reported

above were not explained by group differences in alcohol con-

sumption. Furthermore, the results reported in this paragraph

were not appreciably affected by excluding participants with

co-morbid depressive disorders, cardiovascular disease or history

of drug abuse/dependence. Same patterns of group differences

were observed within the subsample re-scanned at follow-up.

Changes over 1 month of abstinencefrom alcoholThis analysis tested the second hypothesis that reductions of MD

and increases in FA, volumes and increases in concentrations of

NAA and Cho with abstinence from alcohol will be more pro-

nounced in nsADIs than in sADIs. The results are depicted in

Fig. 3.

Fractional anisotropyThe omnibus dMANOVA comparing lobar FA in nsADIs and sADIs

between baseline and follow-up demonstrated a significant effect

for time [F(4,16) = 4.04, P = 0.019] and a trend for an interaction

between smoking status and time [F(4,16) = 2.43, P = 0.09]. The

follow-up ANOVAs demonstrated a significant effect for time

[F(1,19) = 12.9, P = 0.002] and an interaction between smoking

status and time [F(1,19) = 10.57, P = 0.004] in temporal white

matter. These findings appeared to be driven by significant

increases in temporal white matter FA of nsADIs (3.5� 2.6%,

P = 0.003). nsADIs also showed a trend for increasing FA in the

frontal white matter (1.5� 3.0%; P = 0.06), whereas sADIs

demonstrated no FA changes over the same interval (P40.16,

per cent changes 50.57%). Covariation for group differences in

total lifetime consumption of ethanol did not appreciably alter

these findings.

Mean diffusivitydMANOVA yielded no main effects or interactions for smoking

status or time. Although the follow-up repeated measures

ANOVAs pointed to some effects for time and/or time by smoking

status interactions, they were not significant after correction for

multiple comparisons. Importantly and consistent with FA changes,

only nsADIs demonstrated a pattern of MD decrease in white

matter of frontal (�1.5� 2.3%, P = 0.04, uncorrected), temporal

(�1.8� 2.0%, P = 0.01, uncorrected), parietal (�1.8� 2.3%,

P = 0.02, uncorrected) and occipital lobes (�2.6� 4.0%,

P = 0.03, uncorrected). No corresponding changes over the same

interval were observed among sADIs (P40.16, uncorrected, per

cent changes 50.34%). Covariation for group differences in total

lifetime consumption of ethanol did not appreciably alter these

patterns.

VolumesThe omnibus dMANOVA yielded a significant interaction between

smoking status and time [F(4,15) = 3.05, P = 0.05]. This result

reflected pattern of volume increases in frontal (1.2 � 1.5%,

P = 0.01, uncorrected) and temporal white matter (1.5� 2.1%,

P = 0.03, uncorrected) of sADIs, with no patterns of volume

changes in nsADIs (P40.27, uncorrected, percent change

50.5%). Finally, we have obtained similar patterns using monthly

rates of changes. This assures that the observed differences in

recovery between nsADIs and sADIs are not a consequence of

the numerically larger inter-scan interval in nsADIs (37.5 days)

compared with sADIs (31.2 days).

MetabolitesdMANOVAs for NAA, Cho and m-inositol concentrations did not

yield any significant effects of time (P40.27) or time by smoking

status interactions (P40.55).

Finally, additional comparisons indicated that none of the above

results were appreciably influenced by participants with co-morbid

conditions.

Cross-sectional group differences at5-week follow-upIn the smaller longitudinal sample of 21 sADIs and nsADIs, the

group differences between nsADIs and sADIs at 5-week follow-up

and nsLDs re-scanned after 1 year did not reach statistical signif-

icance for lobar white matter MD and FA, lobar white matter

1048 | Brain 2010: 133; 1043–1053 S. Gazdzinski et al.

volumes or mean lobar metabolite concentrations [all

F(8,46)51.57, all P40.23], possibly due to normalization of mag-

netic resonance measures and/or low statistical power.

Relationships between outcome mea-sures at baselineWe tested the hypotheses that in alcohol-dependent individuals

(nsADIs and sADIs combined) at 1 week of abstinence (baseline)

higher lobar MD is associated with lower FA, smaller white matter

volumes and lower concentrations of NAA within the same white

matter regions.

At baseline, larger white matter volumes were moderately

related to lower MD and higher FA, but these associations did

not survive corrections for multiple comparisons. Greater age in

alcohol-dependent individuals was associated with higher MD in

all regions (0.405r50.57, P50.03) and lower FA in frontal and

temporal white matter (r5�0.46, P50.01). After correction for

age, higher FA was associated with lower MD in total white

matter and all lobar regions (r5�0.70, P50.004); smoking

status did not affect these relationships. These patterns remained

significant when participants with co-morbid mood disorders,

hypertension or white matter lesions were excluded from analyses.

In addition, when participants with co-morbid mood disorders only

were excluded, we observed a positive statistical association

between frontal white matter volume and frontal NAA concentra-

tion (r = 0.66, P = 0.008). Finally, a higher amount of alcohol con-

sumed over lifetime (in kilograms) was associated with higher MD

(r = 0.29, P = 0.04) and lower FA (r =�0.34, P = 0.02) in total

white matter, and surprisingly, later onset of heavy drinking was

associated with higher total white matter MD (r = 0.35, P = 0.02).

Otherwise, the FA and MD measures in alcohol-dependent indi-

viduals were not related to the average numbers of drinks per

month over 1, 3 and 8 years, prior to enrolment or with any

measures of smoking severity in sADIs.

Relationships between outcome mea-sures changes over 1 month of absti-nence from alcoholWe hypothesized that over 1 month of abstinence from alcohol,

decreasing lobar white matter MD is associated with correspond-

ing increases in FA, white matter volumes and NAA concentra-

tions, whereas increasing Cho is associated with increasing white

matter volumes in the same regions.

Over the month of abstinence from alcohol and within

alcohol-dependent individuals, the MD decreases were associated

with FA increase in total white matter (r =�0.44, P = 0.02) and

among lobes in frontal (r =�0.56, P = 0.016), temporal (r =�0.49,

P = 0.048) and occipital white matter (r =�0.53, P = 0.024), sup-

porting our hypothesis. There were no patterns of associations

between longitudinal changes of markers obtained with different

magnetic resonance modalities within the same regions and no

such correlation survived corrections for multiple comparisons.

Additionally, when participants with white matter lesions were

excluded from the statistical analyses, the volume increase in

frontal white matter correlated with Cho increase (r = 0.65,

P = 0.044). Later onset of heavy drinking was associated with

faster FA recovery in total white matter (r = 0.52, P = 0.008) and

temporal white matter (r = 0.53, P = 0.024). Finally, none of the

correlations was appreciably affected by exclusion of participants

with depressive disorders, cardiovascular disease or history of drug

abuse/dependence. The associations of volumetric and spectro-

scopic changes with cognitive measures and their changes with

abstinence will be reported elsewhere in a larger cohort.

DiscussionThis is the first report to assess white matter injury in smoking and

non-smoking alcoholics during abstinence using DTI, structural

MRI and 1H MRS imaging together in the same cohort and

within comparable white matter regions.

At 1 week of abstinence from alcohol, nsADIs had higher MD in

frontal, temporal and parietal white matter than nsLDs, whereas

sADIs had higher MD than nsLDs only in frontal white matter.

Longitudinally, over 1 month of abstinence from alcohol, there

was an overall pattern of FA increases and FA by smoking

status interactions, reflecting faster microstructural recovery in

nsADIs than in sADIs. These FA changes were accompanied by

a tendency for MD decrease exclusively in nsADIs. Conversely, the

interactions between time and smoking status for volumetric

changes reflected white matter volume increases in frontal and

temporal white matter of sADIs, but not of nsADIs. Lobar meta-

bolite concentrations did not change significantly over time in

either group. Only FA and MD measures in the same lobes and

their changes were interrelated. At baseline, higher white matter

MD was associated with lower white matter FA. Moreover, later

onset of heavy drinking was associated with faster FA recovery in

total white matter and in temporal white matter, suggesting that

cumulative effects of heavy drinking impairs the ability of white

matter to recover from injury. Taken together, the outcome mea-

sures obtained with different imaging modalities were not signifi-

cantly related to each other and their changes during abstinence

from alcohol demonstrated different patterns of brain injury and

recovery thereof in nsADIs and sADIs. This suggests that each

modality, being differentially sensitive to various brain changes

in alcohol-dependent individuals, provides unique and comple-

mentary information on white matter status cross-sectionally and

during abstinence.

Cross-sectional findingsOur DTI results in lobar white matter are consistent with previous

reports of elevated MD in the corpus callosum of

alcohol-dependent individuals (Pfefferbaum and Sullivan, 2002;

Pfefferbaum et al., 2005a). The corresponding group differences

of FA were not significant, possibly because FA measures within

the same voxels are more affected by crossing fibres (Hirsch et al.,

1999; Alexander et al., 2001) and more susceptible to errors aris-

ing from inclusion of non-white matter voxels than MD

(Pfefferbaum and Sullivan, 2003). Higher lobar MD was signifi-

cantly associated with lower lobar FA, but contrary to Pfefferbaum

White matter recovery in alcoholics Brain 2010: 133; 1043–1053 | 1049

et al. (2005a) who reported associations between callosal FA and

callosal volume, our DTI indices did not relate to the correspond-

ing white matter volumes. However, our results are consistent

with Fjell and colleagues’ (2008), who did not find any consistent

pattern in the relationships between white matter volumes and FA

measures among healthy individuals. Taken together, all the find-

ings suggest that correlations between measures of microstructural

and macrostructural integrity are region-specific and perhaps

restricted to regions of high-density fibre bundles. The lack of

significant volumetric abnormalities in our cohort in the presence

of diffusion abnormalities suggest that DTI is more sensitive to

brain injury than conventional structural MRI, as previously

noted (Pfefferbaum and Sullivan, 2002).

Surprisingly, the nsADIs group demonstrated more widespread

MD abnormalities than the sADIs group. As sADIs consumed more

alcohol over lifetime than nsADIs and started drinking at heavy

levels a full decade earlier than nsADIs, it seems unlikely that less

microstructural injury (lower MD) in sADIs than in nsADIs is due to

the differences in drinking history. Although unexpected, this

result is qualitatively consistent with Paul and colleagues (2008),

who found higher FA in corpus callosum of non-clinical (otherwise

healthy) cigarette smokers compared with their non-smoking

counterparts. They attributed this effect to stimulation of nicotinic

receptors in oligodendrocytes by nicotine that could result in

better microstructural white matter integrity in cigarette smokers.

Nevertheless, less widespread MD abnormalities in sADIs versus

nsADIs may reflect neurodegenerative processes.

Our sADIs had a qualitatively higher degree of white matter

signal hyperintensity than nsADIs, consistent with population stu-

dies (e.g. Jeerakathil et al., 2004), which suggests that smoking

adds an additional burden to white matter integrity. We and

others have demonstrated brain perfusion abnormalities in smok-

ing alcoholics and controls (Rourke et al., 1997; Gazdzinski et al.,

2006). Chronic hypoperfusion results in axonal damage and

demyelination accompanied by formation of redundant myelin

and mild astrogliosis (e.g. Farkas et al., 2004). These processes

could lead to decreased water content within white matter and

the observed lower MD in sADIs versus nsADIs. Alternatively,

chronic cigarette smoking has been equated to a type of repeated

acute (mild) carbon monoxide poisoning (Alonso et al., 2004);

carbon monoxide poisoning is associated with white matter cyto-

toxic oedema and restricted diffusivity in white matter lesions

(see Terajima et al., 2008 and references therein), consistent

with decreased MD.

nsADIs and sADIs demonstrated lower NAA in parietal and fron-

tal white matter, respectively, consistent with most earlier MRS

reports in abstinent alcoholics (Bendszus et al., 2001;

Schweinsburg et al., 2001; Ende et al., 2005; Bartsch et al.,

2007), but not in Parks et al. (2002). We did not observe any

significant Cho abnormalities in white matter, consistent with

Parks et al. (2002), but not Bendszus et al. (2001),

Schweinsburg et al. (2001), Ende et al. (2005) and Bartsch

et al. (2007). Also, similarly to our earlier study of a largely dif-

ferent cohort (Durazzo et al., 2004), sADIs demonstrated patterns

of more NAA abnormalities than nsADIs. The DTI indices were not

related to metabolite concentrations within alcohol-dependent

individuals, similar to the findings by Cader et al. (2007) among

patients with multiple sclerosis. Taken together, these results sug-

gest that DTI and MRS imaging yield complimentary information

on white matter injury. We are aware of published associations

between spectroscopic and DTI measures in cohorts including both

patients and healthy controls, such as in a study that compared a

small cohort of patients with multiple sclerosis with healthy con-

trols (Irwan et al., 2005). In fact, we also observed relationships

between NAA and MD within frontal and parietal white matter of

all alcohol dependent individuals combined and nsLD groups

(r5�0.38, P50.048, corrected), which were absent within

alcohol-dependent individuals and within nsLDs, suggesting that

the correlations were driven by (on average) lower NAA and

higher MD in alcohol-dependent individuals versus nsLDs

(cf. Fig. 2).

Changes over 1 month of abstinencefrom alcoholIn the absence of significant white matter volume increases in

nsADIs, FA increased significantly only in temporal white matter

and this 1 month change was accompanied by a pattern of MD

decreases in multiple white matter regions, suggesting remyelina-

tion and/or recovery of axonal membranes during abstinence from

alcohol in nsADIs. However, much to our surprise, we observed a

Figure 2 MD (A) and FA (B) in frontal white matter of nsLDs,

nsADIs and sADIs. The statistical significance of MD was not

driven by the two nsADIs with highest MD values. Same base-

line patterns were observed among participants who were

rescanned at follow-up.

1050 | Brain 2010: 133; 1043–1053 S. Gazdzinski et al.

consistent pattern of volumetric increases in sADIs that were not

accompanied by changes in DTI indices. In nsADIs, the result

could be interpreted as microstructural recovery, likely due to

remyelination, preceding measurable white matter volume

increases. The sADIs, however, demonstrated qualitatively less

microstructural injury than nsADIs at 1 week of abstinence and

the white matter recovery processes were reflected as significant

volumetric increases. In fact, in a larger sample of abstinent

nsADIs, we have observed no white matter volume changes

over the first month of abstinence, followed by white matter

volume increases over the subsequent 6 months of abstinence.

Similarly, the white matter volumetric increases in sADIs were

also reproduced in a much larger sample of sADIs and this volu-

metric enlargement continued over the ensuing 6 months of absti-

nence (to be reported elsewhere). These different patterns of

white matter volumetric changes with abstinence in sADIs and

nsADIs suggest that the discrepant results of previous volumetric

studies (Pfefferbaum et al., 1995; Agartz et al., 2003) might be

explained by different proportions of smokers and non-smokers in

these cohorts and by the different group patterns of volume

changes during abstinence.

The observed DTI and volumetric changes were not paralleled

by significant NAA increases in either group, suggesting little

metabolic recovery in axons. The lack of accompanying significant

changes in Cho concentrations that are associated with membrane

turnover may reflect a shift in equilibrium between membrane

anabolic and catabolic processes, which does not change the

total amount of choline-containing metabolites. This interpretation

is consistent with a computer tomography study that demon-

strated increasing tissue density during abstinence from alcohol

(Trabert et al., 1995) and with no change in spectroscopic signal

from tissue water (Bartsch et al., 2007), both over about 1 month

of abstinence from alcohol.

LimitationsThe limitations of this study included use of median diffusion indi-

ces calculated over large regions that could be affected adversely

by crossing fibres. The three slices of the MRS image acquisition

spatially limited the brain regions used for analyses. Although we

had a relatively small longitudinal cohort, the patterns of volumet-

ric changes with abstinence are consistent with the results that we

obtained in a significantly larger cohort. The cohort used in our

study included mostly male participants recruited at a Veterans

Administration Medical Centre, so that sex effects of concurrent

alcohol dependence and cigarette smoking could not be assessed.

Figure 3 Patterns of longitudinal frontal white matter changes in (A) MD, (B) FA, (C) volume and (D) NAA concentration observed in

participants who were scanned at baseline and follow-up: the P-values pertain to longitudinal changes. Please, note the significant

decrease in MD of nsADIs (A) is not accompanied by any volume change (C), whereas the volume increase in sADIs (C) is not

accompanied by a MD change (A). The range of corresponding measures in 22 nsLDs is depicted in grey (mean� standard error). NAA

concentrations reported after correction for age and BMI.

White matter recovery in alcoholics Brain 2010: 133; 1043–1053 | 1051

Potential unrecorded differences in nutrition, exercise, general

health and genetic predispositions between study groups may

influence the results described in this study. Finally, future studies

assessing injury and recovery of specific white matter tracts with

higher spatial resolution and/or higher magnetic field strengths

may yield more reliable diffusion indices and allow evaluating spe-

cific white matter neurocircuitry known to be disrupted by sub-

stance dependence.

ConclusionsDifferent imaging modalities seem to provide complimentary infor-

mation and a more detailed understanding of brain injury in alco-

hol dependence and neurordegenerative processes during

abstinence from alcohol. Short-term abstinence from alcohol is

associated with white matter recovery and appears to be promi-

nently driven by its glial component both in smoking and

non-smoking alcoholics. This process may be reflected either as

white matter microstructural improvement in nsADIs or as white

matter volume increases in sADIs. Thus, co-morbid cigarette

smoking may modulate results in studies of neural repair.

We believe our results to be important, as they demonstrate

that use of a single magnetic resonance imaging modality provides

an incomplete picture of neurobiological processes associated with

alcohol induced brain injury and recovery thereof or even lead to

improper interpretation of results.

AcknowledgementsWe thank Mary Rebecca Young and Bill Clift of the San Francisco

VA Substance Abuse Day Hospital and Dr David Pating, Karen

Moise and their colleagues at the San Francisco Kaiser

Permanente Chemical Dependency Recovery Program for their

valuable assistance in recruiting research participants. We thank

Dr Susanne Mueller for help with setting up the Expectation–

Maximization Segmentation procedures.

FundingThis material is the result of work supported with resources

and the use of facilities at the Radiology Research Service of the

Veterans Administration Medical Centre in San Francisco. National

Institutes of Health (AA10788 to D.J.M.); Radiology Research

Service of the Veterans Administration Medical Centre in San

Francisco.

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