A JOURNAL OF NEUROLOGY
Cerebral white matter recovery in abstinent
alcoholics—a multimodality magnetic
Stefan Gazdzinski,1Timothy C. Durazzo,1,2Anderson Mon,2Ping-Hong Yeh1and
Dieter 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,
M. Smoluchowski Institute of Physics,
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 1.5T 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-
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/awp343Brain 2010: 133; 1043–1053 |
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.
For Permissions, please email: firstname.lastname@example.org
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 multivariate
analysis 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-dependent
The 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).
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-
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
et al., 1995).
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
imaging(DTI) studies in
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 samecohort of
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.,
The goals of this study were: (i) to measure white matter vol-
umes, MD, FA and concentrations of major
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
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
1H MRS detectable
Brain 2010: 133; 1043–1053S. 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
Sixteen 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-
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 13.6g of pure ethanol, equivalent of 12oz
beer, 5oz wine or 1.5oz 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
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 1day 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 1day of the scanning session. No
participanthad clinically significant
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.
All magnetic resonance data were obtained on a standard 1.5T 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=5000/100ms, 2.4?2.4?5mm3, 20 con-
tiguous slices covering supratentorial white matter, 3min scan time),
with diffusion sensitizing gradients of b=0, 160, 360, 640 and 1000s/
mm2applied 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=10/7/300ms, 15
flip angle, 1?1?1.5mm3, 7min), for segmentation. Additionally,
axial-oblique double spin-echo (repetition time/echo time1/echo
time2=2500/20/80ms, 1?1?3mm3, 12min) 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 time1H MRS imaging (rep-
etition time/echo time/inversion time=1800/25/165ms, 30min) in
three parallel, oblique-axial slices, each 15mm thick and 6mm
apart and covering the major cerebral lobes (Fig. 1). All data were
carefully reviewed; motion artefacts and overall poor quality were
As 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 alcoholicsBrain 2010: 133; 1043–1053 |
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
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
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
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).
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 analyses
The 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
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:
Measureðfollow ? upÞ ? MeasureðbaselineÞ
Measure ðbaselineÞ ? ðTimebetweenscansinmonthsÞ? 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.
Brain 2010: 133; 1043–1053S. 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).
Table 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-
critandred blood cellcounts
?-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
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 sampleSmaller longitudinal sample
1-year average (drinks/month)
Lifetime average (drinks/month)
Total lifetime consumption of ethanol (kg)
Age at onset of heavy drinking (years)
Major depressive disorder (n)
Substance induced mood disorder (n)
GGT (institutional units)
AST (institutional units)
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; lifetime
average=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=4.7–6.1M/mm3).
White matter recovery in alcoholics Brain 2010: 133; 1043–1053 |
Cross-sectional group comparisons at
This 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
LinearModel indicated significant
[?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-
(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
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 abstinence
This 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
The 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
dMANOVA 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
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
The 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
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).
dMANOVAs 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
Cross-sectional group differences at
In 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
Brain 2010: 133; 1043–1053 S. Gazdzinski et al.
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 baseline
We 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
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 alcohol
We 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.
This is the first report to assess white matter injury in smoking and
non-smoking alcoholics during abstinence using DTI, structural
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
1H MRS imaging together in the same cohort and
Our DTI results in lobar white matter are consistent with previous
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-
ingfrom inclusionof non-white
(Pfefferbaum and Sullivan, 2003). Higher lobar MD was signifi-
cantly associated with lower lobar FA, but contrary to Pfefferbaum
White matter recovery in alcoholicsBrain 2010: 133; 1043–1053 |
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
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
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
et al., 2001;
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 MDin alcohol-dependent
(cf. Fig. 2).
Changes over 1 month of abstinence
In 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.
Brain 2010: 133; 1043–1053S. 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
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.
The 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 |
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-
Different imaging modalities seem to provide complimentary infor-
mation and a more detailed understanding of brain injury in alco-
hol dependenceand neurordegenerative
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.
We thank Mary Rebecca Young and Bill Clift of the San Francisco
VA Substance Abuse Day Hospital and Dr David Pating, Karen
Moiseand their colleagues
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.
This 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
Agartz I, Brag S, Franck J, Hammarberg A, Okugawa G, Svinhufvud K,
et al. MR volumetry during acute alcohol withdrawal and abstinence: a
descriptive study. Alcohol Alcohol 2003; 38: 71–8.
Alexander AL, Hasan KM, Lazar M, Tsuruda JS, Parker DL. Analysis of
partial volume effects in diffusion-tensor MRI. Magn Reson Med
2001; 45: 770–80.
Alonso JR, Cardellach F, Casademont J, Miro O. Reversible inhibition of
mitochondrial complex IV activity in PBMC following acute smoking.
Eur Respir J 2004; 23: 214–18.
Bartsch AJ, Homola G, Biller A, Smith SM, Weijers HG, Wiesbeck GA,
et al. Manifestations of early brain recovery associated with abstinence
from alcoholism. Brain 2007; 130: 36–47.
Bartzokis G, Beckson M, Lu PH, Nuechterlein KH, Edwards N, Mintz J.
Age-related changes in frontal and temporal lobe volumes in men: a
magnetic resonance imaging study. Arch Gen Psychiatry 2001; 58:
Beck AT. Depression Inventory. Philadelphia: Center for Cognitive
Bendszus M, Weijers HG, Wiesbeck G, Warmuth-Metz M, Bartsch AJ,
Engels S, et al. Sequential MR imaging and proton MR spectroscopy in
patients who underwent recent detoxification for chronic alcoholism:
correlation with clinical and neuropsychological data. AJNR Am J
Neuroradiol 2001; 22: 1926–32.
Cader S, Johansen-Berg H, Wylezinska M, Palace J, Behrens TE, Smith S,
et al. Discordant white matter N-acetylasparate and diffusion MRI
measures suggest that chronic metabolic dysfunction contributes to
axonal pathology in multiple sclerosis. Neuroimage 2007; 36: 19–27.
Cardenas VA, Studholme C, Meyerhoff DJ, Song E, Weiner MW.
Chronic active heavy drinking and family history of problem drinking
modulate regional brain tissue volumes. Psychiatry Res 2005; 138:
Durazzo TC, Gazdzinski S, Banys P, Meyerhoff DJ. Cigarette smoking
exacerbates chronic alcohol-induced brain damage: a preliminary
metabolite imaging study. Alcohol Clin Exp Res 2004; 28: 1849–60.
Durazzo TC, Gazdzinski S, Banys P, Meyerhoff DJ. Brain metabolite
concentrations and neurocognition during short-term recovery from
alcohol dependence: preliminary evidence of the effects of concurrent
chronic cigarette smoking. Alcohol Clin Exp Res 2006; 30: 539–51.
Durazzo TC, Gazdzinski S, Meyerhoff DJ. The neurobiological and neu-
rocognitive consequences of chronic cigarette smoking in alcohol use
disorders. Alcohol Alcohol 2007a; 42: 174–85.
Durazzo TC, Rothlind JC, Gazdzinski S, Banys P, Meyerhoff DJ. Chronic
smoking is associated with differential neurocognitive recovery in
abstinent alcoholic patients: a preliminary investigation. Alcohol Clin
Exp Res 2007b; 31: 1114–27.
Ende G, Welzel H, Walter S, Weber-Fahr W, Diehl A, Hermann D, et al.
Monitoring the effects of chronic alcohol consumption and abstinence
on brain metabolism: a longitudinal proton magnetic resonance spec-
troscopy study. Biol Psychiatry 2005.
Fagerstrom KO, Heatherton TF, Kozlowski LT. Nicotine addiction and its
assessment. Ear Nose Throat J 1991; 69: 763–5.
Farkas E, Donka G, de Vos RA, Mihaly A, Bari F, Luiten PG. Experimental
cerebral hypoperfusion induces white matter injury and microglial acti-
vation in the rat brain. Acta Neuropathol 2004; 108: 57–64.
Fergusson DM, Goodwin RD, Horwood LJ. Major depression and cigar-
ette smoking: results of a 21-year longitudinal study. Psychol Med
2003; 33: 1357–67.
Fjell AM, Westlye LT, Greve DN, Fischl B, Benner T, van der Kouwe AJ,
et al. The relationship between diffusion tensor imaging and volumetry
as measures of white matter properties. Neuroimage 2008.
Gazdzinski S, Durazzo T, Jahng GH, Ezekiel F, Banys P, Meyerhoff D.
Effects of chronic alcohol dependence and chronic cigarette smoking
on cerebral perfusion: a preliminary magnetic resonance study. Alcohol
Clin Exp Res 2006; 30: 947–58.
Gazdzinski S, Durazzo TC, Yeh PH, Hardin D, Banys P, Meyerhoff DJ.
Chronic cigarette smoking modulates injury and short-term recovery of
the medial temporal lobe in alcoholics. Psychiatry Res 2008a; 162:
Gazdzinski S, Kornak J, Weiner MW, Meyerhoff DJ. Body mass index
and magnetic resonance markers of brain integrity in adults. Ann
Neurol 2008b; 63: 652–7.
Hasin DS, Stinson FS, Ogburn E, Grant BF. Prevalence, correlates, dis-
ability, and comorbidity of DSM-IV alcohol abuse and dependence in
the United States: results from the National Epidemiologic Survey on
Brain 2010: 133; 1043–1053S. Gazdzinski et al.
Alcohol and Related Conditions. Arch Gen Psychiatry 2007; 64:
Hirsch JG, Bock M, Essig M, Schad LR. Comparison of diffusion aniso-
tropy measurements in combination with the flair-technique. Magn
Reson Imaging 1999; 17: 705–16.
Irwan R, Sijens PE, Potze JH, Oudkerk M. Correlation of proton MR
spectroscopy and diffusion tensor imaging. Magn Reson Imaging
2005; 23: 851–8.
Jeerakathil T, Wolf PA, Beiser A, Massaro J, Seshadri S, D’Agostino RB,
et al. Stroke risk profile predicts white matter hyperintensity volume:
the Framingham Study. Stroke 2004; 35: 1857–61.
Lim KO, Hedehus M, Moseley M, de Crespigny A, Sullivan EV,
Pfefferbaum A. Compromised white matter tract integrity in schizo-
phrenia inferred from diffusion tensor imaging. Arch Gen Psychiatry
1999; 56: 367–74.
Meyerhoff D, Blumenfeld R, Truran D, Lindgren J, Flenniken D,
Cardenas V, et al. Effects of heavy drinking, binge drinking, and
family history of alcoholism on regional brain metabolites. Alcohol
Clin Exp Res 2004; 28: 650–61.
Parks MH, Dawant BM, Riddle WR, Hartmann SL, Dietrich MS,
Nickel MK, et al. Longitudinal brain metabolic characterization of
chronic alcoholics with proton magnetic resonance spectroscopy.
Alcohol Clin Exp Res 2002; 26: 1368–80.
Paul RH, Grieve SM, Niaura R, David SP, Laidlaw DH, Cohen R, et al.
Chronic cigarette smoking and the microstructural integrity of white
matter in healthy adults: a diffusion tensor imaging study. Nicotine
Tob Res 2008; 10: 137–47.
Pfefferbaum A, Adalsteinsson E, Sullivan EV. Dysmorphology and micro-
structural degradation of the corpus callosum: interaction of age and
alcoholism. Neurobiol Aging 2006; 27: 994–1009.
Pfefferbaum A, Adalsteinsson E, Sullivan EV. Frontal circuitry degradation
marks healthy adult aging: evidence from diffusion tensor imaging.
Neuroimage 2005b; 26: 891–9.
Pfefferbaum A, Sullivan EV. Microstructural but not macrostructural dis-
ruptionof white matter inwomen
Neuroimage 2002; 15: 708–18.
Pfefferbaum A, Sullivan EV. Increased brain white matter diffusivity in
normal adult aging: relationship to anisotropy and partial voluming.
Magn Reson Med 2003; 49: 953–61.
Pfefferbaum A, Sullivan EV, Mathalon DH, Shear PK, Rosenbloom MJ,
Lim KO. Longitudinal changes in magnetic resonance imaging brain
volumes in abstinent and relapsed alcoholics. Alcohol Clin Exp Res
1995; 19: 1177–91.
Pierpaoli C, Barnett A, Pajevic S, Chen R, Penix LR, Virta A, et al. Water
diffusion changes in Wallerian degeneration and their dependence on
white matter architecture. Neuroimage 2001; 13: 1174–85.
Reese TG, Heid O, Weisskoff RM, Wedeen VJ. Reduction of eddy-cur-
rent-induced distortion in diffusion MRI using a twice-refocused spin
echo. Magn Reson Med 2003; 49: 177–82.
Romberger DJ, Grant K. Alcohol consumption and smoking status: the
role of smoking cessation. Biomed Pharmacother 2004; 58: 77–83.
Ross B, Bluml S. Magnetic resonance spectroscopy of the human brain.
Anat Rec 2001; 265: 54–84.
Rourke SB, Dupont RM, Grant I, Lehr PP, Lamoureux G, Halpern S, et al.
Reduction in cortical IMP-SPET tracer uptake with recent cigarette
consumption in a young group of healthy males. San Diego HIV
Neurobehavioral Research Center. Eur J Nucl Med 1997; 24: 422–7.
Schuff N, Capizzano AA, Du AT, Amend DL, O’Neill J, Norman D, et al.
Selective reduction of N-acetylaspartate in medial temporal and par-
ietal lobes in AD. Neurology 2002; 58: 928–35.
Schweinsburg BC, Taylor MJ, Alhassoon OM, Videen JS, Brown GG,
Patterson TL, et al. Chemical pathology in brain white matter of
recently detoxified alcoholics: a 1H magnetic resonance spectroscopy
investigation of alcohol-associated frontal lobe injury. Alcohol Clin Exp
Res 2001; 25: 924–34.
Shear PK, Jernigan TL, Butters N. Volumetric magnetic resonance ima-
ging quantification of longitudinal brain changes in abstinent alcoholics
[published erratum appears in Alcohol Clin Exp Res 1994; 18: 766].
Alcohol Clin Exp Res 1994; 18: 172–6.
Skinner HA, Sheu WJ. Reliability of alcohol use indices. The lifetime
drinking history and the MAST. J Stud Alcohol 1982; 43: 1157–70.
Sobell LC, Sobell MB. Timeline follow-back: a technique for assessing
self-reported alcohol consumption. In: Litten R, Allen J, editors.
Measuring Alcohol Consumption. Towota, NJ: The Humana Press
Inc.; 1992. p. 41–72.
Sobell LC, Sobell MB, Riley DM, Schuller R, Pavan DS, Cancilla A, et al.
The reliability of alcohol abusers’ self-reports of drinking and life
events that occurred in the distant past. J Stud Alcohol 1988; 49:
Song SK, Yoshino J, Le TQ, Lin SJ, Sun SW, Cross AH, et al.
Demyelination increases radial diffusivity in corpus callosum of
mouse brain. Neuroimage 2005; 26: 132–40.
Sotak CH. Nuclear magnetic resonance (NMR) measurement of the
apparent diffusion coefficient (ADC) of tissue water and its relationship
to cell volume changes in pathological states. Neurochem Int 2004;
Spielberger CD, Gorsuch RL, Lushene R, Vagg PR, Jacobs GA. Self-
Evaluation Questionaire. Palo Alto, CA: Consulting Psychologist
Sullivan EV. NIAAA Research Monograph No. 34: human brain vulner-
ability toalcoholism: evidence
Noronha A, Eckardt M, Warren K, editors. Review of NIAAA’s neu-
roscience and behavioral research portfolio. Bethesda, MD: National
Institute on Alcohol Abuse and Alcoholism; 2000. p. 473–508.
Sullivan J, Sykora K, Schneiderman J, Naranjo C, Sellers E. Assesment of
alcohol withdrawal: the revised clinical institute withdrawl assesment
for alcohol scale. Br J Addict 1989; 84: 1353–7.
Tabachnick BG, Fidell LS. Using Multivariate Statistics. 4th edn. Allyn &
Bacon, Needham Heights, MA: 2001.
Takahashi S, Yonezawa H, Takahashi J, Kudo M, Inoue T, Tohgi H.
Selective reduction of diffusion anisotropy in white matter of
Alzheimer disease brains measured by 3.0 Tesla magnetic resonance
imaging. Neurosci Lett 2002; 332: 45–8.
Terajima K, Igarashi H, Hirose M, Matsuzawa H, Nishizawa M,
Nakada T. Serial assessments of delayed encephalopathy after
carbon monoxide poisoning using magnetic resonance spectroscopy
and diffusion tensor imaging on 3.0T system. Eur Neurol 2008; 59:
Trabert W, Betz T, Niewald M, Huber G. Significant reversibility of alco-
holic brain shrinkage within 3 weeks of abstinence. Acta Psychiatr
Scand 1995; 92: 87–90.
Van Leemput K, Maes F, Vandermeulen D, Suetens P. Automated
model-based tissue classification of MR images of the brain. IEEE
Trans Med Imaging 1999; 18: 897–908.
Wang JJ, Durazzo TC, Gazdzinski S, Yeh PH, Mon A, Meyerhoff DJ.
MRSI and DTI: a multimodal approach for improved detection of
white matter abnormalities in alcohol and nicotine dependence.
NMR Biomed 2009; 22: 516–22.
Weinrebe W, Graf-Gruss R, Schwabe R, Stippler D, Fusgen I. The two-
factor method–a new approach to categorizing the clinical stages of
malnutrition in geriatric patients. J Am Geriatr Soc 2002; 50: 2105–7.
Yeh PH, Simpson K, Durazzo TC, Gazdzinski S, Meyerhoff DJ. Tract-
based spatial statistics (TBSS) of diffusion tensor imaging data in alco-
hol dependence: abnormalities of the motivational neurocircuitry.
Psychiatry Res Neuroimaging 2009; 173: 22–30.
White matter recovery in alcoholics Brain 2010: 133; 1043–1053 |