Context
Despite 25 years of structural imaging in bipolar disorder, brain regions affected in the disorder are ill defined.
Objectives
To use meta-analytical techniques to investigate structural brain changes in bipolar disorder and to assess the effect of medication use and demographic and clinical variables.
Data Sources
The MEDLINE, EMBASE, and PsycINFO databases were searched from 1980-2007 for studies using magnetic resonance imaging or x-ray computed tomography to compare brain structure in patients with bipolar disorder and controls.
Study Selection
We identified 1471 unique publications from which 141 studies were included in a database and 98 were selected for meta-analysis.
Data Extraction
Twenty-six demographic and clinical variables were extracted from each study where available. For the meta-analysis, mean structure size and standard deviation were extracted for continuous variables, and numbers of patients and controls with an abnormality were extracted for binary variables.
Data Synthesis
Bipolar disorder was associated with lateral ventricle enlargement (effect size = 0.39; 95% confidence interval, 0.24-0.55; P = 8×10−7) and increased rates of deep white matter hyperintensities (odds ratio = 2.49; 95% confidence interval, 1.64-3.79; P = 2×10−5) but not periventricular hyperintensities. Gray matter volume increased among patients when the proportion of patients using lithium increased (P = .004). Calculations from this meta-analysis show current imaging studies (which typically examine 8 regions) have a 34% chance of making a type I error. Type II errors are also appreciable (for example, 70% when measuring the lateral ventricular volume in a typical study involving 25 patients and 33 controls).
Conclusions
The meta-analyses revealed robust but regionally nonspecific changes of brain structure in bipolar disorder. Individual studies will remain underpowered unless sample size is increased or improvements in phenotypic selection and imaging methods are made to reduce within-study heterogeneity. The provision of online databases, as illustrated herein, may facilitate a more refined design and analysis of structural imaging data sets in bipolar disorder.
Despite 25 years of structural neuroimaging of patients with bipolar disorder, including more than 7000 magnetic resonance imaging (MRI) scans, there remains considerable debate over the sensitivity and specificity of structural brain changes in bipolar disorder. Studies continue to report conflicting findings, such as both significantly larger or smaller volumes of the amygdala,1,2 hippocampus,3,4 and thalamus3,5 among patients with bipolar disorder. Meta-analyses are beginning to reveal consistent abnormalities in bipolar disorder, such as increased rates of hyperintensities6,7 and perhaps lateral ventricular enlargement.8,9 Studies may be contradictory because of between-study heterogeneity in the patient and control groups in terms of medication use10,11 and demographic12,13 and clinical variables14,15 and because individual studies may suffer from high rates of type I and type II errors. There is good evidence to suggest medication may affect brain structure; cross-sectional studies16-18 and a longitudinal study19 have suggested lithium increases gray matter volume, possibly through its neurotrophic effects,20 and typical neuroleptic medication may be associated with striatal enlargement.21,22 In addition, type I and II errors are prevalent because of the large number of measures made in individual studies and small sample sizes, respectively. Finally, although effect sizes may be calculated from individual studies, there is little published information from meta-analyses on the pooled Cohen effect sizes of structural differences between patients with bipolar disorder and controls, making it difficult to accurately calculate a priori the number of participants necessary to show a significant group difference.
In the present meta-analysis, we directly address these problems while extending the scope and methods of previous meta-analyses. The number of studies included is approximately 4 times the largest previous meta-analysis9 in bipolar disorder. We have maximized the number of brain regions analyzed by comprehensively listing all brain structures reported in 3 or more studies. In addition, we used meta-regression techniques to investigate the effect of medication use and clinical and demographic variables, which had not been attempted previously. We present the results as Cohen effect sizes (with a correction for small sample sizes), enabling researchers to calculate subject numbers required for future studies to be sufficiently powered. In addition, we present a small supplementary meta-analysis comparing patients with bipolar disorder to patients with schizophrenia (when included as a subgroup in a bipolar study) to evaluate the diagnostic specificity of our findings. Finally, we provide an online database of structural imaging results in bipolar disorder, listing 26 clinical and demographic variables, where available, from 141 studies.
The study was divided into 2 parts, the construction of a database of 141 studies investigating structural abnormalities in bipolar disorder and a meta-analysis comparing patients with bipolar disorder to controls from a subgroup of 98 studies in the database. A smaller, supplementary meta-analysis compared brain structure between patients with bipolar disorder and schizophrenia from a subgroup of 23 studies.
The inclusion criteria for the database required peer-reviewed studies that made a structural brain measure using x-ray computed tomography (CT) or MRI in patients with bipolar disorder and a control group. We excluded case studies, reviews, publications without standard diagnostic criteria, studies combining patients with bipolar disorder and major depressive disorder, duplicate publications, and investigations using voxel-based morphometry, which cannot be included in a traditional meta-analysis. The MEDLINE, EMBASE, and PsycINFO databases were searched up to October 2007 using relevant expanded subject headings and free text searches; detailed search terms are available from the authors on request. A total of 1471 unique publications were examined. One hundred forty-one publications fulfilled the inclusion criteria and were included in the database.
Data recorded in the database
The following were recorded from each study where available: number of patients with bipolar disorder and controls, mean (SD) age, number of females in the patient and control groups, diagnostic classification system (eg, DSM-IV), and number of patients with bipolar I and bipolar II disorder. Patients with bipolar disorder were assumed to have bipolar I disorder if described as having mania or psychosis. For current medication use within the bipolar group, we recorded the number of patients who were described as being drug free, using mood stabilizers, or taking lithium, anticonvulsants, sodium valproate, carbamazepine, antipsychotics, antidepressants, or benzodiazepines. In addition, the number of patients previously treated with electroconvulsive therapy was recorded if available. For each study, we recorded all structures or abnormalities measured, the number of separate measurements made, if the measurement was MRI or CT based, the field strength of the MRI scanner, and slice thickness.
Database statistical analysis
Because the majority of variables in the database were not normally distributed, correlations were assessed in SPSS 15.0 (SPSS Inc, Chicago, Illinois) using the Spearman ρ. Power calculations were carried out using GPOWER 2.0.23
Identification of brain regions/abnormalities to be included in the meta-analysis
To ensure no bias in selecting brain regions/abnormalities for the meta-analysis, we recorded every structure or abnormality investigated in the 141 studies. As with previous meta-analyses, exact anatomical definitions of individual structures varied across studies. For a given structure, some studies reported left and right measurements separately, while others reported the total combined measure. In this meta-analysis, the left, right, and total measurements were treated as separate measures. To ensure the meta-analysis was sufficiently powered, brain region measures were included if there were 3 or more studies reporting a mean and standard deviation in both the control and patient groups (continuous measures), and abnormalities were included if there were 3 or more studies reporting the number of patients and controls with the abnormality (binary measure). After this process, 47 regions or abnormalities from a total of 98 studies were selected for the bipolar vs control meta-analysis and 12 regions/abnormalities from 23 studies were selected for the bipolar vs schizophrenia meta-analysis. Eight studies using CT measures of total lateral ventricle volume were included in the meta-analyses; all other brain structures were imaged using MRI.
The meta-analyses were performed in STATA 9.2 (StataCorp, College Station, Texas) using the METAN command. For continuous outcome measures (eg, volume of a brain region), Hedges g was used, which is Cohen effect size with a correction for bias from small sample sizes.24 This metric is commonly used in meta-analyses and is representative of the difference in structural measurement between the control and patient distribution. However, we also show percentage difference effect size to aid biological interpretation of the data.9 While the majority of studies report absolute volume measures, some studies report volumes as ratios of the entire brain or cross-sectional area measures. All such measurements have been included in the meta-analysis. However, as combining measures may increase heterogeneity, an additional analysis was carried out with volume measures only (see “Sensitivity Analysis” subsection).
For binary outcome measures (eg, number of patients and controls with deep white matter hyperintensities), the odds ratio was used. Outcome measures from each study were rechecked on a separate occasion by the same investigator (M.J.K.) to ensure accuracy. In addition, no inconsistencies were found when a second investigator (U.E.) verified a random sample of 50 sets of outcome measures.
Combining study estimates
A separate meta-analysis was performed for each brain region/abnormality. Where 2 or more studies reported similar patient or control demographics, we contacted the authors directly to verify there was not a significant overlap in the sample. Outcome measures were combined using a random-effects, inverse-weighted variance model (DerSimonian and Laird method).25 Because the bipolar vs control meta-analysis examined a large number of regions, type I errors should be considered, and thus, results that pass Bonferroni correction for multiple comparisons are indicated.
A minority of imaging studies presented measures from patient subgroups rather than a combined patient group. In such cases, we entered the subgroups into the meta-analysis as if they were separate studies, with the number of subjects in the control group being divided by the number of patient subgroups. Where studies reported males and females separately, we entered the results as if they were from 2 separate studies, a technique adopted by a previous meta-analysis.26
Assessing between-study heterogeneity
To test for between-study heterogeneity, the Cochran Q test statistic was used, and where P < .10, the studies were concluded to be heterogeneous.27 The I2 statistic (equal to the percentage of total variation across studies due to heterogeneity) was used to aid interpretability of between-study heterogeneity.28
Publication bias was investigated for regions where the pooled effect size revealed a significant group difference and where at least 5 studies were included in the meta-analysis. Although publication bias may be assessed by visually inspecting a funnel plot, we used the Egger regression test, which is a more quantitative method of assessing publication bias.29 Evidence of bias is indicated if the intercept of a regression line of effect size/standard error against 1/standard error significantly deviates from zero.
Meta-regression of clinical variables and study quality
The effects of clinical variables and study quality were assessed using a random-effects meta-regression implemented using the METAREG command in STATA 9.2. The default option using residual maximum likelihood was selected. To avoid type I errors, demographic and clinical variables were chosen based on key clinical questions and the availability of the variables reported in studies.30 For structures where there was a significant difference between patients with bipolar disorder and controls, we investigated whether effect size was modulated by study quality. Study quality was scored in 6 key areas by 2 independent investigators (M.J.K. and U.E.), with disputes resolved by consensus. One point was given for each of the following categories: age matching (not stated/significant difference = 0, matched = 1), sex matching (not stated/significant difference = 0, matched = 1), control subjects had no psychiatric illness (not stated = 0, no psychiatric illness = 1), same CT/MRI scanner and sequence used for each subject (different scanner or sequence = 0, same scanner and sequence = 1), good reliability of measures (intraclass correlation coefficient/κ <0.8/not stated = 0, intraclass correlation coefficient/κ ≥0.8 = 1), and small slice thickness (≥4 mm = 0, >1.5 mm and <4 mm = 0.5, and ≤1.5 mm = 1).
To test how robust the results were to variations in meta-analysis inputs, we examined the effect of the following: (1) percentage difference in the patient mean volume compared with the control mean volume as an outcome measure for continuous data (the calculation of this effect size and the effect size variance has been described in more detail in previous meta-analytical studies)9,26; (2) excluding studies with patients with bipolar II disorder; and (3) excluding studies reporting continuous data as area, length, or ratios rather than absolute volume.
Demographic and clinical data from the database are reported, followed by results from the meta-analyses.
One hundred forty-one studies, including 3509 patients with bipolar disorder and 4687 controls, were entered into the database (Table 1). Table 2 summarizes the variables recorded. Bipolar disorder was defined using DSM-IV (69 studies), DSM-III-R (38 studies), DSM-III (22 studies), Research Diagnostic Criteria (11 studies), and International Statistical Classification of Diseases, 10th Revision (1 study). For image acquisition, 125 studies used MRI and 16 studies used CT imaging. Among MRI studies, 78% used a field strength of 1.5 T, 18% used lower field strength, and 3% used higher field strength. The mean (SD) slice thickness was 9.3 (1.0) mm in CT studies and 3.5 (2.5) mm in MRI studies. The Bipolar Disorder Neuroimaging Database (BiND) is freely available athttp://www.bipolardatabase.org.
Trends in study variables over time
Studies did not recruit a larger number of subjects over time (R = −0.10; P = .22) (Figure 1), although the number of studies per year increased (R = 0.79; P < .001) (Figure 2). The mean age of participants decreased during the review period (patients, R = −0.34; P < .001; controls, R = −0.34; P < .001), with recent studies recruiting adolescent patients. In addition, studies recorded increasing numbers of demographic variables over time (R = 0.50; P < .001).
Implications for type i errors in individual studies
The mean number of regions or abnormalities measured per study was 8, and there was a negative correlation between the number of measures made and the total number of subjects included in each study (R = −0.23; P = .007).
Different regions/ abnormalities measured
From the 141 studies, 377 different regions or abnormalities were measured. Only 47 were analyzed by 3 or more studies and hence were included in the bipolar vs control meta-analysis. Twelve structures were also measured in patients with schizophrenia in 3 or more studies, and these were included in the bipolar vs schizophrenia meta-analysis.
Meta-analysis comparing patients with bipolar disorder to control subjects
Patients with bipolar disorder showed increased volumes of the total lateral ventricles, right lateral ventricle, and third ventricle and decreased cross-sectional area of the corpus callosum (Table 3) (Figure 3). Hyperintensities, deep white matter hyperintensities, subcortical gray matter hyperintensities, and hyperintensities in the left hemisphere, right hemisphere, and frontal and parietal lobe were more frequently observed in patients with bipolar disorder (Table 4) (Figure 4). Analysis of the occipital and temporal lobes was not possible because of low numbers of hyperintensities reported in these regions. Increased total lateral ventricular volume, hyperintensities, deep white matter hyperintensities, and hyperintensities in the right hemisphere remained significant after Bonferroni correction.
Of the 13 affected regions/abnormalities, 10 were reported by enough studies to perform a publication bias test. There was evidence of significant publication bias for the combined hyperintensities category and hyperintensities in the right hemisphere (Table 3 and Table 4).
Meta-regression and investigation of heterogeneity
Nineteen of the 47 brain regions/abnormalities examined showed significant between-study heterogeneity, justifying the use of a random-effects model to combine the effect sizes. The following meta-regression analysis investigated possible sources of this heterogeneity. To investigate whether lateral ventricle expansion may be present at the beginning of the illness or may progress with time, we examined the effect of duration of illness and age on the difference in total lateral ventricular volume between patients and controls. No effect of mean patient age (n = 15 studies; P = .66) or duration of illness (n = 11 studies; P = .36) was detected. Because hyperintensities have reportedly been increased in older patients with depression and patients with a late age at onset,157,158 we investigated the effects of these variables on the difference in incidence rates of deep white matter lesions between patients with bipolar disorder and controls. However, there was no significant effect of age (n = 13 studies; P = .60) or age at onset (n = 8; P = .42). The proportion of patients using lithium in a given study had no observable effect on the incidence of deep white matter hyperintensities in patients compared with controls (n = 4 studies; P = .46). Because lithium has been reported to increase gray matter volume,19 we performed a meta-regression investigating whether the number of patients taking lithium modulated the effect size of patient and control differences in total gray matter. Gray matter volume increased among patients compared with controls when the proportion of patients using lithium increased (n = 8 studies; P = .004). Amygdala volume change was especially heterogeneous between studies; however, this heterogeneity was not explained by differences in patient sex or age (data not shown). There was no significant effect of study quality score for any of the structures differing between patients with bipolar disorder and controls (P > .15 in all cases).
Older studies measured the lateral ventricles using CT imaging, rather than MRI, and/or reported lateral ventricle to brain ratio (VBR) rather than a volume measure. To assess if these measures affected the results, we combined 9 MRI studies measuring the lateral ventricle, giving an effect size of 0.26 (95% confidence interval [CI], 0.07 to 0.45; P = .007), and compared this with 8 CT studies that gave a combined effect size of 0.52 (95% CI, 0.28 to 0.76; P < .001). When combining 10 studies reporting a VBR measure, we obtained an effect size of 0.44 (95% CI, 0.20 to 0.68; P < .001), while 6 studies reporting a volume measure gave an effect size of 0.28 (95% CI, 0.06 to 0.49; P = .0082).
The results of the sensitivity analysis are shown in Table 5.
Meta-analysis comparing patients with bipolar disorder to patients with schizophrenia
The left lateral ventricle and third ventricle were smaller in patients with bipolar disorder compared to patients with schizophrenia (Table 6). Both the left and right hippocampus were larger in patients with bipolar disorder compared with patients with schizophrenia, although there was evidence of publication bias for these measures.
Summary of structural changes in bipolar disorder
Patients with bipolar disorder had lateral ventricular enlargement (+17%) and increased rates of deep white matter hyperintensities (2.5 times more likely in patients than controls) but did not have increased rates of periventricular hyperintensities. From the meta-regression analysis, there was no evidence that age, age at onset, or use of lithium affected rates of deep white matter hyperintensities or that duration of illness increased ventricular enlargement. However, lithium use was associated with increased total gray matter volume. Meta-regression is statistically low powered and may be prone to type II errors; in addition, associations found at the level of multiple studies may not exist at the individual-patient level.159 Despite this, the association of lithium use with increased gray matter volume has been reported in a number of individual studies,16,18,19 supporting our finding.
Given the size of the meta-analysis, the relatively small number of significant findings is perhaps surprising. There may be genuinely limited structural change in bipolar disorder, or between-study heterogeneity may have obscured other differences. A large number of factors may affect between-study heterogeneity, and some parameters, such as variations in brain region definitions and scanner sequences, are difficult to examine with meta-regression techniques. In this sense, meta-analyses are limited and well-controlled imaging studies with very large sample sizes may provide more definitive answers.
Biological implications of main findings
Ventricular enlargement has been extensively documented in schizophrenia,26 although it is not clear if the expansion is due to diffuse or focal gray/white matter volume reduction. Although the neuropathological mechanism for this change is not known, the volume loss may be due in part to smaller neuronal cell bodies and fewer dendritic spines and dendritic arborizations on pyramidal neurones reportedly found in patients with schizophrenia.160 In bipolar disorder, ventricular expansion and corresponding reduction in brain volume may be linked to the reduced population of glial cells and neuronal density observed in this condition.161 It is not clear if ventricular enlargement occurs before, during, or after illness onset, although our meta-regression suggests enlargement does not progress with illness duration and so may be present near the beginning of the illness.
Increased rates of hyperintensities are not specific to bipolar disorder, being associated with major depressive disorder, normal aging, dementia, cardiovascular disease, and elevated diastolic blood pressure.162 Postmortem studies of subjects with depression using in vitro MRI have reported that hyperintensities represent dilated perivascular spaces, oligemic demyelination, and ischemic demyelination.163
Individual studies have high rates of type i and type ii errors
To provide sufficient statistical power, studies that measured a large number of regions would also need to recruit a large number of subjects. Indeed, if there were a consensus for the expected effect size for differences in cerebral structures between patients with bipolar disorder and control subjects, and a consensus for controlling multiple comparisons, one would expect to see a positive correlation between the number of subjects in a study and the number of measurements made. In contrast, there was a significant negative correlation, suggesting there is no consensus in one or both of these issues.
Typically, 8 regions were investigated per study, giving the probability of a type I error as 0.34 (calculation from Bonferroni equation), unless a correction for multiple comparisons is made. This estimate is an upper boundary for false-positive error rates, assuming regional brain measures are independent. Studies are not only at risk for type I errors; for example, if a study measured the volume of the lateral ventricles, which was associated with one of the largest effect sizes in the meta-analysis (effect size = 0.39), and recruited the mean number of patients and controls per study calculated from the database (25 and 33 respectively), the study would have a 70% chance of making a type II error. If the effect size for lateral ventricle dilation is calculated from MRI studies only, a representative study has an 84% chance of making a type II error. Hence, a typical structural imaging study is underpowered to detect one of the largest effect sizes found in the meta-analysis. To combat these problems, studies will need to recruit larger numbers of patients and controls and have a consistent way of dealing with multiple comparisons.
Despite the aforementioned problems, recruitment size has not changed during the last 25 years and studies remain underpowered. It is possible type I errors have actually misled researchers into believing that 20 to 30 patients and controls is a sufficient sample size because previous studies have “obtained results” with these numbers of participants. For a future study to be sufficiently powered, the required number of subjects may be readily calculated from the effect sizes given in Table 3. For example, for a study to be sufficiently powered to detect a difference in lateral ventricle volume, 105 patients and 105 controls would be required (power = 0.8; α = .05, 2-tailed t test). The power calculations and effect sizes are based on studies included in the meta-analysis, and the prediction of required sample sizes for future studies assumes within-study heterogeneity will remain constant. However, if future studies were to minimize within-study heterogeneity through refined phenotype selection and improved imaging methods, the effect sizes may increase, leading to smaller required sample sizes. In addition, where a structure strongly correlates with age and brain volume, the required number of subjects may also be smaller because using these variables as nuisance covariates will increase the power of the analysis. Small studies are still important but should perhaps be cautious in their conclusions and combine their result with previous studies and report an “updated effect size.”
Comparison with previously published meta-analyses
Our study was in good agreement with previous meta-analyses of brain structure in bipolar disorder. Previously reported odds ratios of increased rates of hyperintensities among patients with bipolar disorder were 3.296 and 3.3,7 which are in close agreement with the odds ratio of 3.04 calculated in this study. We extended these findings by performing meta-analyses on the location and subtype of hyperintensities. Compared with a previous meta-analysis of patients with affective disorder,8 we found a similar effect size for increased total lateral ventricular volume among patients with bipolar disorder (d = 0.42 and d = 0.39, respectively). Our results also concur with a previous meta-analysis showing right lateral ventricle enlargement in bipolar disorder.9
In our study, patients with schizophrenia compared to bipolar disorder showed enlargement of the left lateral ventricle and third ventricle and decreased hippocampal volume. However, relatively few brain structures were included because of the small number of studies directly comparing these diagnostic groups. For a more comprehensive comparison, we compared our bipolar-control meta-analysis with a previous meta-analysis by Wright et al26 comparing patients with schizophrenia with controls (Figure 5). This comparison supports our own findings as well as suggests that patients with schizophrenia show increased volume of the total and right lateral ventricle, reduced volume of the amygdala, and perhaps increased volume of the globus pallidus compared to patients with bipolar disorder. For cortical structures, both our meta-analysis and the Wright et al meta-analysis pooled studies that measured gray matter volume alone with studies that combined gray and white matter. Combining such studies may dilute the effect of regional reductions in cortical gray matter, an example being superior temporal lobe volumes in schizophrenia, where reviews by Shenton et al164 and McCarley et al165 have highlighted that gray matter reductions are obscured when gray and white matter are analyzed together. However, when we separated studies based on this criterion, results in our meta-analysis did not change (data not shown). Two meta-analyses of hippocampus volume in major depressive disorder166,167 report a volumetric reduction, which contrasts with our null finding in bipolar disorder. The possible neuroprotective effects of lithium use may have masked the reduction in hippocampal volume in bipolar disorder.19,168 To test this hypothesis, we performed a meta-regression of the proportion of patients using lithium on the effect size of total hippocampal volume. The effect was close to significant (n = 4 studies; P = .051); as the number of patients using lithium increased, the volume of the hippocampus compared with controls also increased, supporting our hypothesis. Individual structural imaging studies in bipolar disorder that have directly investigated correlations between lithium use and hippocampus volume are equivocal, with 4 studies reporting that lithium use was associated with hippocampus enlargement,4,148,155,156 while 2 reported no effect.108,116
A meta-analysis involving more studies may have increased power to detect significant differences; however, studies included may be more heterogeneous, increasing the variance and hence reducing the chance of detecting a significant difference. The rationale used for the present meta-analysis was that the advantage of including more studies outweighed the disadvantage from increased heterogeneity due to different types of measurement. Where the number of studies was large enough, we attempted subanalyses, such as volume and ratio measures of lateral ventricle volume, to reduce heterogeneity and give further specificity to the findings and also attempted to account for study heterogeneity by implementing a meta-regression analysis. Lateral ventricular volume measured using CT was associated with a larger effect size than MRI studies. This may be because older CT studies were less stringent in controlling for possible confounds, such as alcohol and drug abuse/dependence, which are relatively common in bipolar disorder169 and are associated with brain volume change.170 Computed tomographic studies were also more likely to report the VBR measure; because this measurement reduces variance due to brain volume, it may be more sensitive to ventricular dilation in bipolar disorder compared with absolute volume measures.
The results database and meta-analysis are publicly available on the Internet for the purpose of allowing researchers to verify the methods used, aiding upcoming studies and reviews, and enabling further meta-analyses. Customized meta-regressions may be carried out on the data by individual researchers interested in the effect of various combinations of demographic and clinical variables on a region of interest (eg, the effect of age and sex on temporal lobe volume in bipolar disorder).
In conclusion, there are robust but limited changes in brain structure in bipolar disorder and evidence that lithium medication increases gray matter volume. Without refinements in phenotypic selection and imaging methods or increased sample size, type I and type II errors will remain appreciable. Future studies would benefit by providing comprehensive patient clinical data as well as continuing to provide raw structural measures to facilitate future meta-analyses. The publicly available results database and meta-analysis from this article may prove to be a useful resource for planning future structural imaging studies.
Correspondence: Matthew J. Kempton, MSci, MSc, PhD, Centre for Neuroimaging Sciences, PO89, Institute of Psychiatry, DeCrespigny Park, London SE5 8AF, England (matthew.kempton@iop.kcl.ac.uk).
Submitted for Publication: July 2, 2007; final revision received March 31, 2008; accepted March 31, 2008.
Author Contributions: Dr Kempton had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Financial Disclosure: None reported.
Funding/Support: Dr Kempton was supported by a Medical Research Council studentship and was funded in part by the National Institute for Health Research (NIHR) Biomedical Research Centre for Mental Health at King's College London, Institute of Psychiatry, and South London and Maudsley NHS Foundation Trust. Dr Ettinger is funded by an NIHR Personal Award.
Disclaimer: The views expressed in this publication are those of the authors and not necessarily those of the National Health Service, NIHR, or Department of Health.
Additional Contributions: We thank S. Landau, PhD, of the Department of Biostatistics at the Institute of Psychiatry for a helpful discussion regarding the meta-analysis methods.
1.Chang
KKarchemskiy
ABarnea-Goraly
NGarrett
ASimeonova
DIReiss
A Reduced amygdalar gray matter volume in familial pediatric bipolar disorder.
J Am Acad Child Adolesc Psychiatry 2005;44
(6)
565- 573
PubMedGoogle Scholar 2.Altshuler
LLBartzokis
GGrieder
TCurran
JJimenez
TLeight
KWilkins
JGerner
RMintz
J An MRI study of temporal lobe structures in men with bipolar disorder or schizophrenia.
Biol Psychiatry 2000;48
(2)
147- 162
PubMedGoogle Scholar 3.Frazier
JAChiu
SBreeze
JLMakris
NLange
NKennedy
DNHerbert
MRBent
EKKoneru
VKDieterich
MEHodge
SMRauch
SLGrant
PECohen
BMSeidman
LJCaviness
VSBiederman
J Structural brain magnetic resonance imaging of limbic and thalamic volumes in pediatric bipolar disorder.
Am J Psychiatry 2005;162
(7)
1256- 1265
PubMedGoogle Scholar 4.Beyer
JLKuchibhatla
MPayne
MEMoo-Young
MCassidy
FMacfall
JKrishnan
KR Hippocampal volume measurement in older adults with bipolar disorder.
Am J Geriatr Psychiatry 2004;12
(6)
613- 620
PubMedGoogle Scholar 5.Dupont
RMJernigan
TLHeindel
WButters
NShafer
KWilson
THesselink
JGillin
JC Magnetic resonance imaging and mood disorders: localization of white matter and other subcortical abnormalities.
Arch Gen Psychiatry 1995;52
(9)
747- 755
PubMedGoogle Scholar 6.Altshuler
LLCurran
JGHauser
PMintz
JDenicoff
KPost
R T2 hyperintensities in bipolar disorder: magnetic resonance imaging comparison and literature meta-analysis.
Am J Psychiatry 1995;152
(8)
1139- 1144
PubMedGoogle Scholar 7.Videbech
P MRI findings in patients with affective disorder: a meta-analysis.
Acta Psychiatr Scand 1997;96
(3)
157- 168
PubMedGoogle Scholar 8.Elkis
HFriedman
LWise
AMeltzer
HY Meta-analyses of studies of ventricular enlargement and cortical sulcal prominence in mood disorders: comparisons with controls or patients with schizophrenia.
Arch Gen Psychiatry 1995;52
(9)
735- 746
PubMedGoogle Scholar 9.McDonald
CZanelli
JRabe-Hesketh
SEllison-Wright
ISham
PKalidindi
SMurray
RMKennedy
N Meta-analysis of magnetic resonance imaging brain morphometry studies in bipolar disorder.
Biol Psychiatry 2004;56
(6)
411- 417
PubMedGoogle Scholar 10.Gulseren
SGurcan
MGulseren
LGelal
FErol
A T2 hyperintensities in bipolar patients and their healthy siblings.
Arch Med Res 2006;37
(1)
79- 85
PubMedGoogle Scholar 11.López-Larson
MPDelBello
MPZimmerman
MESchwiers
MLStrakowski
SM Regional prefrontal gray and white matter abnormalities in bipolar disorder.
Biol Psychiatry 2002;52
(2)
93- 100
PubMedGoogle Scholar 12.de Asis
JMGreenwald
BSAlexopoulos
GSKiosses
DNAshtari
MHeo
MYoung
RC Frontal signal hyperintensities in mania in old age.
Am J Geriatr Psychiatry 2006;14
(7)
598- 604
PubMedGoogle Scholar 13.Kaur
SSassi
RBAxelson
DNicoletti
MBrambilla
PMonkul
ESHatch
JPKeshavan
MSRyan
NBirmaher
BSoares
JC Cingulate cortex anatomical abnormalities in children and adolescents with bipolar disorder.
Am J Psychiatry 2005;162
(9)
1637- 1643
PubMedGoogle Scholar 14.Haznedar
MMRoversi
FPallanti
SBaldini-Rossi
NSchnur
DBLicalzi
EMTang
CHof
PRHollander
EBuchsbaum
MS Fronto-thalamo-striatal gray and white matter volumes and anisotropy of their connections in bipolar spectrum illnesses.
Biol Psychiatry 2005;57
(7)
733- 742
PubMedGoogle Scholar 15.Zimmerman
MEDelBello
MPGetz
GEShear
PKStrakowski
SM Anterior cingulate subregion volumes and executive function in bipolar disorder.
Bipolar Disord 2006;8
(3)
281- 288
PubMedGoogle Scholar 16.Sassi
RBNicoletti
MBrambilla
PMallinger
AGFrank
EKupfer
DJKeshavan
MSSoares
JC Increased gray matter volume in lithium-treated bipolar disorder patients.
Neurosci Lett 2002;329
(2)
243- 245
PubMedGoogle Scholar 17.Kieseppä
Tvan Erp
TGHaukka
JPartonen
TCannon
TDPoutanen
VPKaprio
JLonnqvist
J Reduced left hemispheric white matter volume in twins with bipolar I disorder.
Biol Psychiatry 2003;54
(9)
896- 905
PubMedGoogle Scholar 18.Bearden
CEThompson
PMDalwani
MHayashi
KMLee
ADNicoletti
MTrakhtenbroit
MGlahn
DCBrambilla
PSassi
RBMallinger
AGFrank
EKupfer
DJSoares
JC Greater cortical gray matter density in lithium-treated patients with bipolar disorder.
Biol Psychiatry 2007;62
(1)
7- 16
PubMedGoogle Scholar 19.Moore
GJBebchuk
JMWilds
IBChen
GManji
HK Lithium-induced increase in human brain grey matter [published correction appears in
Lancet. 2000;356(9247):2104].
Lancet 2000;356
(9237)
1241- 1242
PubMedGoogle Scholar 20.Fukumoto
TMorinobu
SOkamoto
YKagaya
AYamawaki
S Chronic lithium treatment increases the expression of brain-derived neurotrophic factor in the rat brain.
Psychopharmacology (Berl) 2001;158
(1)
100- 106
PubMedGoogle Scholar 21.Chakos
MHLieberman
JABilder
RMBorenstein
MLerner
GBogerts
BWu
HKinon
BAshtari
M Increase in caudate nuclei volumes of first-episode schizophrenic patients taking antipsychotic drugs.
Am J Psychiatry 1994;151
(10)
1430- 1436
PubMedGoogle Scholar 22.Keshavan
MSBagwell
WWHaas
GLSweeney
JASchooler
NRPettegrew
JW Changes in caudate volume with neuroleptic treatment.
Lancet 1994;344
(8934)
1434
PubMedGoogle Scholar 23. GPOWER: A priori, post-hoc, and compromise power analyses for MS-DOS [computer program]. Version 2.0. Bonn, Germany Bonn University Dept of Psychology1992;
24.Hedges
LVOlkin
I Statistical Methods for Meta-analysis. Orlando, FL Academic Press1985;
25.DerSimonian
RLaird
N Meta-analysis in clinical trials.
Control Clin Trials 1986;7
(3)
177- 188
PubMedGoogle Scholar 26.Wright
ICRabe-Hesketh
SWoodruff
PWDavid
ASMurray
RMBullmore
ET Meta-analysis of regional brain volumes in schizophrenia.
Am J Psychiatry 2000;157
(1)
16- 25
PubMedGoogle Scholar 27.Sutton
AJ Methods for Meta-analysis in Medical Research. Chichester, NY Wiley2000;
28.Higgins
JPThompson
SGDeeks
JJAltman
DG Measuring inconsistency in meta-analyses.
BMJ 2003;327
(7414)
557- 560
PubMedGoogle Scholar 29.Egger
MDavey Smith
GSchneider
MMinder
C Bias in meta-analysis detected by a simple, graphical test.
BMJ 1997;315
(7109)
629- 634
PubMedGoogle Scholar 30.Thompson
SGHiggins
JP How should meta-regression analyses be undertaken and interpreted?
Stat Med 2002;21
(11)
1559- 1573
PubMedGoogle Scholar 31.Nasrallah
HAJacoby
CGMcCalley-Whitters
M Cerebellar atrophy in schizophrenia and mania.
Lancet 1981;1
(8229)
1102
PubMedGoogle Scholar 32.Lippmann
SManshadi
MBaldwin
HDrasin
GRice
JAlrajeh
S Cerebellar vermis dimensions on computerized tomographic scans of schizophrenic and bipolar patients.
Am J Psychiatry 1982;139
(5)
667- 668
PubMedGoogle Scholar 33.Nasrallah
HAMcCalley-Whitters
MJacoby
CG Cerebral ventricular enlargement in young manic males: a controlled CT study.
J Affect Disord 1982;4
(1)
15- 19
PubMedGoogle Scholar 34.Nasrallah
HAMcCalley-Whitters
MJacoby
CG Cortical atrophy in schizophrenia and mania: a comparative CT study.
J Clin Psychiatry 1982;43
(11)
439- 441
PubMedGoogle Scholar 35.Rangel-Guerra
RAPerez-Payan
HMinkoff
LTodd
LE Nuclear magnetic resonance in bipolar affective disorders.
AJNR Am J Neuroradiol 1983;4
(3)
229- 231
PubMedGoogle Scholar 36.Pearlson
GDGarbacz
DJTompkins
RHAhn
HSGutterman
DFVeroff
AEDePaulo
JR Clinical correlates of lateral ventricular enlargement in bipolar affective disorder.
Am J Psychiatry 1984;141
(2)
253- 256
PubMedGoogle Scholar 37.Lippmann
SManshadi
MBaldwin
HDrasin
GWagemaker
HRice
JAlrajeh
S Cerebral CAT scan imaging in schizophrenic and bipolar patients.
J Ky Med Assoc 1985;83
(1)
13- 15
PubMedGoogle Scholar 38.Pearlson
GDGarbacz
DJMoberg
PJAhn
HSDePaulo
JR Symptomatic, familial, perinatal, and social correlates of computerized axial tomography (CAT) changes in schizophrenics and bipolars.
J Nerv Ment Dis 1985;173
(1)
42- 50
PubMedGoogle Scholar 39.Dewan
MJHaldipur
CVLane
EDonnelly
MPBoucher
MMajor
LF Normal cerebral asymmetry in bipolar patients.
Biol Psychiatry 1987;22
(9)
1058- 1066
PubMedGoogle Scholar 40.Dupont
RMJernigan
TLGillin
JCButters
NDelis
DCHesselink
JR Subcortical signal hyperintensities in bipolar patients detected by MRI.
Psychiatry Res 1987;21
(4)
357- 358
PubMedGoogle Scholar 41.Yates
WRJacoby
CGAndreasen
NC Cerebellar atrophy in schizophrenia and affective disorder.
Am J Psychiatry 1987;144
(4)
465- 467
PubMedGoogle Scholar 42.Dewan
MJHaldipur
CVBoucher
MMajor
LF Is CT ventriculomegaly related to hypercortisolemia?
Acta Psychiatr Scand 1988;77
(2)
230- 231
PubMedGoogle Scholar 43.Dewan
MJHaldipur
CVLane
EEIspahani
ABoucher
MFMajor
LF Bipolar affective disorder, I: comprehensive quantitative computed tomography.
Acta Psychiatr Scand 1988;77
(6)
670- 676
PubMedGoogle Scholar 44.Iacono
WGSmith
GNMoreau
MBeiser
MFleming
JALin
TYFlak
B Ventricular and sulcal size at the onset of psychosis.
Am J Psychiatry 1988;145
(7)
820- 824
PubMedGoogle Scholar 45.Hauser
PDauphinais
IDBerrettini
WDeLisi
LEGelernter
JPost
RM Corpus callosum dimensions measured by magnetic resonance imaging in bipolar affective disorder and schizophrenia.
Biol Psychiatry 1989;26
(7)
659- 668
PubMedGoogle Scholar 46.Johnstone
ECOwens
DGCrow
TJFrith
CDAlexandropolis
KBydder
GColter
N Temporal lobe structure as determined by nuclear magnetic resonance in schizophrenia and bipolar affective disorder.
J Neurol Neurosurg Psychiatry 1989;52
(6)
736- 741
PubMedGoogle Scholar 47.Andreasen
NCSwayze
V
IIFlaum
MAlliger
RCohen
G Ventricular abnormalities in affective disorder: clinical and demographic correlates.
Am J Psychiatry 1990;147
(7)
893- 900
PubMedGoogle Scholar 48.Coffman
JABornstein
RAOlson
SCSchwarzkopf
SBNasrallah
HA Cognitive impairment and cerebral structure by MRI in bipolar disorder.
Biol Psychiatry 1990;27
(11)
1188- 1196
PubMedGoogle Scholar 49.Dolan
RJPoynton
AMBridges
PKTrimble
MR Altered magnetic resonance white-matter T1 values in patients with affective disorder.
Br J Psychiatry 1990;157107- 110
PubMedGoogle Scholar 50.Dupont
RMJernigan
TLButters
NDelis
DHesselink
JRHeindel
WGillin
JC Subcortical abnormalities detected in bipolar affective disorder using magnetic resonance imaging: clinical and neuropsychological significance.
Arch Gen Psychiatry 1990;47
(1)
55- 59
PubMedGoogle Scholar 51.Harvey
IWilliams
MToone
BKLewis
SWTurner
SWMcGuffin
P The ventricular-brain ratio (VBR) in functional psychoses: the relationship of lateral ventricular and total intracranial area.
Psychol Med 1990;20
(1)
55- 62
PubMedGoogle Scholar 52.Swayze
VW
IIAndreasen
NCAlliger
RJEhrhardt
JCYuh
WT Structural brain abnormalities in bipolar affective disorder: ventricular enlargement and focal signal hyperintensities.
Arch Gen Psychiatry 1990;47
(11)
1054- 1059
PubMedGoogle Scholar 53.Altshuler
LLConrad
AHauser
PLi
XMGuze
BHDenikoff
KTourtellotte
WPost
R Reduction of temporal lobe volume in bipolar disorder: a preliminary report of magnetic resonance imaging.
Arch Gen Psychiatry 1991;48
(5)
482- 483
PubMedGoogle Scholar 54.Figiel
GSKrishnan
KRRao
VPDoraiswamy
MEllinwood
EH
JrNemeroff
CBEvans
DBoyko
O Subcortical hyperintensities on brain magnetic resonance imaging: a comparison of normal and bipolar subjects.
J Neuropsychiatry Clin Neurosci 1991;3
(1)
18- 22
PubMedGoogle Scholar 55.Lewine
RRRisch
SCRisby
EStipetic
MJewart
RDEccard
MCaudle
JPollard
W Lateral ventricle-brain ratio and balance between CSF HVA and 5-HIAA in schizophrenia.
Am J Psychiatry 1991;148
(9)
1189- 1194
PubMedGoogle Scholar 56.McDonald
WMKrishnan
KRDoraiswamy
PMBlazer
DG Occurrence of subcortical hyperintensities in elderly subjects with mania.
Psychiatry Res 1991;40
(4)
211- 220
PubMedGoogle Scholar 57.Brown
FWLewine
RJHudgins
PARisch
SC White matter hyperintensity signals in psychiatric and nonpsychiatric subjects.
Am J Psychiatry 1992;149
(5)
620- 625
PubMedGoogle Scholar 58.Risch
SCLewine
RJKalin
NHJewart
RDRisby
EDCaudle
JMStipetic
MTurner
JEccard
MBPollard
WE Limbic-hypothalamic-pituitary-adrenal axis activity and ventricular-to-brain ratio studies in affective illness and schizophrenia.
Neuropsychopharmacology 1992;6
(2)
95- 100
PubMedGoogle Scholar 59.Swayze
VW
IIAndreasen
NCAlliger
RJYuh
WTEhrhardt
JC Subcortical and temporal structures in affective disorder and schizophrenia: a magnetic resonance imaging study.
Biol Psychiatry 1992;31
(3)
221- 240
PubMedGoogle Scholar 60.Strakowski
SMWoods
BTTohen
MWilson
DRDouglass
AWStoll
AL MRI subcortical signal hyperintensities in mania at first hospitalization.
Biol Psychiatry 1993;33
(3)
204- 206
PubMedGoogle Scholar 61.Strakowski
SMWilson
DRTohen
MWoods
BTDouglass
AWStoll
AL Structural brain abnormalities in first-episode mania.
Biol Psychiatry 1993;33
(8-9)
602- 609
PubMedGoogle Scholar 62.Aylward
EHRoberts-Twillie
JVBarta
PEKumar
AJHarris
GJGeer
MPeyser
CEPearlson
GD Basal ganglia volumes and white matter hyperintensities in patients with bipolar disorder.
Am J Psychiatry 1994;151
(5)
687- 693
PubMedGoogle Scholar 63.Bullmore
EBrammer
MHarvey
IPersaud
RMurray
RRon
M Fractal analysis of the boundary between white matter and cerebral cortex in magnetic resonance images: a controlled study of schizophrenic and manic-depressive patients.
Psychol Med 1994;24
(3)
771- 781
PubMedGoogle Scholar 64.Harvey
IPersaud
RRon
MABaker
GMurray
RM Volumetric MRI measurements in bipolars compared with schizophrenics and healthy controls.
Psychol Med 1994;24
(3)
689- 699
PubMedGoogle Scholar 65.Kato
TShioiri
TMurashita
JHamakawa
HInubushi
TTakahashi
S Phosphorus-31 magnetic resonance spectroscopy and ventricular enlargement in bipolar disorder.
Psychiatry Res 1994;55
(1)
41- 50
PubMedGoogle Scholar 66.Schlaepfer
TEHarris
GJTien
AYPeng
LWLee
SFederman
EBChase
GABarta
PEPearlson
GD Decreased regional cortical gray matter volume in schizophrenia.
Am J Psychiatry 1994;151
(6)
842- 848
PubMedGoogle Scholar 67.Botteron
KNVannier
MWGeller
BTodd
RDLee
BC Preliminary study of magnetic resonance imaging characteristics in 8- to 16-year-olds with mania.
J Am Acad Child Adolesc Psychiatry 1995;34
(6)
742- 749
PubMedGoogle Scholar 68.Dupont
RMButters
NSchafer
KWilson
THesselink
JGillin
JC Diagnostic specificity of focal white matter abnormalities in bipolar and unipolar mood disorder.
Biol Psychiatry 1995;38
(7)
482- 486
PubMedGoogle Scholar 69.Lewine
RRHudgins
PBrown
FCaudle
JRisch
SC Differences in qualitative brain morphology findings in schizophrenia, major depression, bipolar disorder, and normal volunteers.
Schizophr Res 1995;15
(3)
253- 259
PubMedGoogle Scholar 70.Ohaeri
JUAdeyinka
AOEnyidah
SNOsuntokun
BO Schizophrenic and manic brains in Nigerians: computerised tomography findings.
Br J Psychiatry 1995;166
(4)
496- 500
PubMedGoogle Scholar 71.Woods
BTYurgelun-Todd
DMikulis
DPillay
SS Age-related MRI abnormalities in bipolar illness: a clinical study.
Biol Psychiatry 1995;38
(12)
846- 847
PubMedGoogle Scholar 72.Shioiri
TOshitani
YKato
TMurashita
JHamakawa
HInubushi
TNagata
TTakahashi
S Prevalence of cavum septum pellucidum detected by MRI in patients with bipolar disorder, major depression and schizophrenia.
Psychol Med 1996;26
(2)
431- 434
PubMedGoogle Scholar 73.Drevets
WCPrice
JLSimpson
JR
JrTodd
RDReich
TVannier
MRaichle
ME Subgenual prefrontal cortex abnormalities in mood disorders.
Nature 1997;386
(6627)
824- 827
PubMedGoogle Scholar 74.Pearlson
GDBarta
PEPowers
REMenon
RRRichards
SSAylward
EHFederman
EBChase
GAPetty
RGTien
AY Ziskind-Somerfeld Research Award 1996: medial and superior temporal gyral volumes and cerebral asymmetry in schizophrenia versus bipolar disorder.
Biol Psychiatry 1997;41
(1)
1- 14
PubMedGoogle Scholar 75.Persaud
RRussow
HHarvey
ILewis
SWRon
MMurray
RMdu Boulay
G Focal signal hyperintensities in schizophrenia.
Schizophr Res 1997;27
(1)
55- 64
PubMedGoogle Scholar 76.Zipursky
RBSeeman
MVBury
ALangevin
RWortzman
GKatz
R Deficits in gray matter volume are present in schizophrenia but not bipolar disorder.
Schizophr Res 1997;26
(2-3)
85- 92
PubMedGoogle Scholar 77.Altshuler
LLBartzokis
GGrieder
TCurran
JMintz
J Amygdala enlargement in bipolar disorder and hippocampal reduction in schizophrenia: an MRI study demonstrating neuroanatomic specificity.
Arch Gen Psychiatry 1998;55
(7)
663- 664
PubMedGoogle Scholar 78.Roy
PDZipursky
RBSaint-Cyr
JABury
ALangevin
RSeeman
MV Temporal horn enlargement is present in schizophrenia and bipolar disorder.
Biol Psychiatry 1998;44
(6)
418- 422
PubMedGoogle Scholar 79.Bilder
RMWu
HBogerts
BAshtari
MRobinson
DWoerner
MLieberman
JADegreef
G Cerebral volume asymmetries in schizophrenia and mood disorders: a quantitative magnetic resonance imaging study.
Int J Psychophysiol 1999;34
(3)
197- 205
PubMedGoogle Scholar 80.Dasari
MFriedman
LJesberger
JStuve
TAFindling
RLSwales
TPSchulz
SC A magnetic resonance imaging study of thalamic area in adolescent patients with either schizophrenia or bipolar disorder as compared to healthy controls.
Psychiatry Res 1999;91
(3)
155- 162
PubMedGoogle Scholar 81.DelBello
MPStrakowski
SMZimmerman
MEHawkins
JMSax
KW MRI analysis of the cerebellum in bipolar disorder: a pilot study.
Neuropsychopharmacology 1999;21
(1)
63- 68
PubMedGoogle Scholar 82.Friedman
LFindling
RLKenny
JTSwales
TPStuve
TAJesberger
JALewin
JSSchulz
SC An MRI study of adolescent patients with either schizophrenia or bipolar disorder as compared to healthy control subjects.
Biol Psychiatry 1999;46
(1)
78- 88
PubMedGoogle Scholar 83.Lim
KORosenbloom
MJFaustman
WOSullivan
EVPfefferbaum
A Cortical gray matter deficit in patients with bipolar disorder.
Schizophr Res 1999;40
(3)
219- 227
PubMedGoogle Scholar 84.McDonald
WMTupler
LAMarsteller
FAFigiel
GSDiSouza
SNemeroff
CBKrishnan
KR Hyperintense lesions on magnetic resonance images in bipolar disorder.
Biol Psychiatry 1999;45
(8)
965- 971
PubMedGoogle Scholar 85.Sax
KWStrakowski
SMZimmerman
MEDelBello
MPKeck
PE
JrHawkins
JM Frontosubcortical neuroanatomy and the continuous performance test in mania.
Am J Psychiatry 1999;156
(1)
139- 141
PubMedGoogle Scholar 86.Strakowski
SMDelBello
MPSax
KWZimmerman
MEShear
PKHawkins
JMLarson
ER Brain magnetic resonance imaging of structural abnormalities in bipolar disorder.
Arch Gen Psychiatry 1999;56
(3)
254- 260
PubMedGoogle Scholar 87.Young
RCNambudiri
DEJain
Hde Asis
JMAlexopoulos
GS Brain computed tomography in geriatric manic disorder.
Biol Psychiatry 1999;45
(8)
1063- 1065
PubMedGoogle Scholar 88.Hauser
PMatochik
JAltshuler
LLDenicoff
KDConrad
ALi
XPost
RM MRI-based measurements of temporal lobe and ventricular structures in patients with bipolar I and bipolar II disorders.
J Affect Disord 2000;60
(1)
25- 32
PubMedGoogle Scholar 89.Hirayasu
YMcCarley
RWSalisbury
DFTanaka
SKwon
JSFrumin
MSnyderman
DYurgelun-Todd
DKikinis
RJolesz
FAShenton
ME Planum temporale and Heschl gyrus volume reduction in schizophrenia: a magnetic resonance imaging study of first-episode patients.
Arch Gen Psychiatry 2000;57
(7)
692- 699
PubMedGoogle Scholar 90.Krabbendam
LHonig
AWiersma
JVuurman
EFHofman
PADerix
MMNolen
WAJolles
J Cognitive dysfunctions and white matter lesions in patients with bipolar disorder in remission.
Acta Psychiatr Scand 2000;101
(4)
274- 280
PubMedGoogle Scholar 91.Rabins
PVAylward
EHolroyd
SPearlson
G MRI findings differentiate between late-onset schizophrenia and late-life mood disorder.
Int J Geriatr Psychiatry 2000;15
(10)
954- 960
PubMedGoogle Scholar 92.Brambilla
PHarenski
KNicoletti
MAMallinger
AGFrank
EKupfer
DJKeshavan
MSSoares
JC Anatomical MRI study of basal ganglia in bipolar disorder patients.
Psychiatry Res 2001;106
(2)
65- 80
PubMedGoogle Scholar 93.Brambilla
PHarenski
KNicoletti
MMallinger
AGFrank
EKupfer
DJKeshavan
MSSoares
JC MRI study of posterior fossa structures and brain ventricles in bipolar patients.
J Psychiatr Res 2001;35
(6)
313- 322
PubMedGoogle Scholar 94.Brambilla
PHarenski
KNicoletti
MMallinger
AGFrank
EKupfer
DJKeshavan
MSSoares
JC Differential effects of age on brain gray matter in bipolar patients and healthy individuals.
Neuropsychobiology 2001;43
(4)
242- 247
PubMedGoogle Scholar 95.Caetano
SCSassi
RBrambilla
PHarenski
KNicoletti
MMallinger
AGFrank
EKupfer
DJKeshavan
MSSoares
JC MRI study of thalamic volumes in bipolar and unipolar patients and healthy individuals.
Psychiatry Res 2001;108
(3)
161- 168
PubMedGoogle Scholar 96.McIntosh
AMForrester
ALawrie
SMByrne
MHarper
AKestelman
JNBest
JJJohnstone
ECOwens
DG A factor model of the functional psychoses and the relationship of factors to clinical variables and brain morphology.
Psychol Med 2001;31
(1)
159- 171
PubMedGoogle Scholar 97.Moore
PBShepherd
DJEccleston
DMacmillan
ICGoswami
UMcAllister
VLFerrier
IN Cerebral white matter lesions in bipolar affective disorder: relationship to outcome.
Br J Psychiatry 2001;178172- 176
PubMedGoogle Scholar 98.Moore
PBEl-Badri
SMCousins
DShepherd
DJYoung
AHMcAllister
VLFerrier
IN White matter lesions and season of birth of patients with bipolar affective disorder.
Am J Psychiatry 2001;158
(9)
1521- 1524
PubMedGoogle Scholar 99.Noga
JTVladar
KTorrey
EF A volumetric magnetic resonance imaging study of monozygotic twins discordant for bipolar disorder.
Psychiatry Res 2001;106
(1)
25- 34
PubMedGoogle Scholar 100.Sassi
RBNicoletti
MBrambilla
PHarenski
KMallinger
AGFrank
EKupfer
DJKeshavan
MSSoares
JC Decreased pituitary volume in patients with bipolar disorder.
Biol Psychiatry 2001;50
(4)
271- 280
PubMedGoogle Scholar 101.Brambilla
PNicoletti
MAHarenski
KSassi
RBMallinger
AGFrank
EKupfer
DJKeshavan
MSSoares
JC Anatomical MRI study of subgenual prefrontal cortex in bipolar and unipolar subjects.
Neuropsychopharmacology 2002;27
(5)
792- 799
PubMedGoogle Scholar 102.Getz
GEDelBello
MPFleck
DEZimmerman
MESchwiers
MLStrakowski
SM Neuroanatomic characterization of schizoaffective disorder using MRI: a pilot study.
Schizophr Res 2002;55
(1-2)
55- 59
PubMedGoogle Scholar 103.Lyoo
IKLee
HKJung
JHNoam
GGRenshaw
PF White matter hyperintensities on magnetic resonance imaging of the brain in children with psychiatric disorders.
Compr Psychiatry 2002;43
(5)
361- 368
PubMedGoogle Scholar 104.Pillai
JJFriedman
LStuve
TATrinidad
SJesberger
JALewin
JSFindling
RLSwales
TPSchulz
SC Increased presence of white matter hyperintensities in adolescent patients with bipolar disorder.
Psychiatry Res 2002;114
(1)
51- 56
PubMedGoogle Scholar 105.Strakowski
SMDelBello
MPZimmerman
MEGetz
GEMills
NPRet
JShear
PAdler
CM Ventricular and periventricular structural volumes in first- versus multiple-episode bipolar disorder.
Am J Psychiatry 2002;159
(11)
1841- 1847
PubMedGoogle Scholar 106.Bertolino
AFrye
MCallicott
JHMattay
VSRakow
RShelton-Repella
JPost
RWeinberger
DR Neuronal pathology in the hippocampal area of patients with bipolar disorder: a study with proton magnetic resonance spectroscopic imaging.
Biol Psychiatry 2003;53
(10)
906- 913
PubMedGoogle Scholar 107.Blumberg
HPKaufman
JMartin
AWhiteman
RZhang
JHGore
JCCharney
DSKrystal
JHPeterson
BS Amygdala and hippocampal volumes in adolescents and adults with bipolar disorder.
Arch Gen Psychiatry 2003;60
(12)
1201- 1208
PubMedGoogle Scholar 108.Brambilla
PHarenski
KNicoletti
MSassi
RBMallinger
AGFrank
EKupfer
DJKeshavan
MSSoares
JC MRI investigation of temporal lobe structures in bipolar patients.
J Psychiatr Res 2003;37
(4)
287- 295
PubMedGoogle Scholar 109.Brambilla
PNicoletti
MASassi
RBMallinger
AGFrank
EKupfer
DJKeshavan
MSSoares
JC Magnetic resonance imaging study of corpus callosum abnormalities in patients with bipolar disorder.
Biol Psychiatry 2003;54
(11)
1294- 1297
PubMedGoogle Scholar 110.Sassi
RBBrambilla
PNicoletti
MMallinger
AGFrank
EKupfer
DJKeshavan
MSSoares
JC White matter hyperintensities in bipolar and unipolar patients with relatively mild-to-moderate illness severity.
J Affect Disord 2003;77
(3)
237- 245
PubMedGoogle Scholar 111.Sharma
VMenon
RCarr
TJDensmore
MMazmanian
DWilliamson
PC An MRI study of subgenual prefrontal cortex in patients with familial and non-familial bipolar I disorder.
J Affect Disord 2003;77
(2)
167- 171
PubMedGoogle Scholar 112.Silverstone
TMcPherson
HLi
QDoyle
T Deep white matter hyperintensities in patients with bipolar depression, unipolar depression and age-matched control subjects.
Bipolar Disord 2003;5
(1)
53- 57
PubMedGoogle Scholar 113.Ahn
KHLyoo
IKLee
HKSong
ICOh
JSHwang
JKwon
JKim
MJKim
MRenshaw
PF White matter hyperintensities in subjects with bipolar disorder.
Psychiatry Clin Neurosci 2004;58
(5)
516- 521
PubMedGoogle Scholar 114.Beyer
JLKuchibhatla
MPayne
MMoo-Young
MCassidy
FMacFall
JKrishnan
KR Caudate volume measurement in older adults with bipolar disorder.
Int J Geriatr Psychiatry 2004;19
(2)
109- 114
PubMedGoogle Scholar 115.Brambilla
PNicoletti
MSassi
RBMallinger
AGFrank
EKeshavan
MSSoares
JC Corpus callosum signal intensity in patients with bipolar and unipolar disorder.
J Neurol Neurosurg Psychiatry 2004;75
(2)
221- 225
PubMedGoogle Scholar 116.Chen
BKSassi
RAxelson
DHatch
JPSanches
MNicoletti
MBrambilla
PKeshavan
MSRyan
NDBirmaher
BSoares
JC Cross-sectional study of abnormal amygdala development in adolescents and young adults with bipolar disorder.
Biol Psychiatry 2004;56
(6)
399- 405
PubMedGoogle Scholar 117.Chen
HHNicoletti
MAHatch
JPSassi
RBAxelson
DBrambilla
PMonkul
ESKeshavan
MSRyan
NDBirmaher
BSoares
JC Abnormal left superior temporal gyrus volumes in children and adolescents with bipolar disorder: a magnetic resonance imaging study.
Neurosci Lett 2004;363
(1)
65- 68
PubMedGoogle Scholar 118.Chen
HHNicoletti
MSanches
MHatch
JPSassi
RBAxelson
DBrambilla
PKeshavan
MSRyan
NBirmaher
BSoares
JC Normal pituitary volumes in children and adolescents with bipolar disorder: a magnetic resonance imaging study.
Depress Anxiety 2004;20
(4)
182- 186
PubMedGoogle Scholar 119.Connor
SENg
VMcDonald
CSchulze
KMorgan
KDazzan
PMurray
RM A study of hippocampal shape anomaly in schizophrenia and in families multiply affected by schizophrenia or bipolar disorder.
Neuroradiology 2004;46
(7)
523- 534
PubMedGoogle Scholar 120.Davis
KAKwon
ACardenas
VADeicken
RF Decreased cortical gray and cerebral white matter in male patients with familial bipolar I disorder.
J Affect Disord 2004;82
(3)
475- 485
PubMedGoogle Scholar 121.DelBello
MPZimmerman
MEMills
NPGetz
GEStrakowski
SM Magnetic resonance imaging analysis of amygdala and other subcortical brain regions in adolescents with bipolar disorder.
Bipolar Disord 2004;6
(1)
43- 52
PubMedGoogle Scholar 122.Hirashima
FParow
AMStoll
ALDemopulos
CMDamico
KERohan
MLEskesen
JGZuo
CSCohen
BMRenshaw
PF Omega-3 fatty acid treatment and T(2) whole brain relaxation times in bipolar disorder.
Am J Psychiatry 2004;161
(10)
1922- 1924
PubMedGoogle Scholar 123.Sassi
RBBrambilla
PHatch
JPNicoletti
MAMallinger
AGFrank
EKupfer
DJKeshavan
MSSoares
JC Reduced left anterior cingulate volumes in untreated bipolar patients.
Biol Psychiatry 2004;56
(7)
467- 475
PubMedGoogle Scholar 124.Supprian
TReiche
WSchmitz
BGrunwald
IBackens
MHofmann
EGeorg
TFalkai
PReith
W MRI of the brainstem in patients with major depression, bipolar affective disorder and normal controls.
Psychiatry Res 2004;131
(3)
269- 276
PubMedGoogle Scholar 125.Blumberg
HPFredericks
CWang
FKalmar
JHSpencer
LPapademetris
XPittman
BMartin
APeterson
BSFulbright
RKKrystal
JH Preliminary evidence for persistent abnormalities in amygdala volumes in adolescents and young adults with bipolar disorder.
Bipolar Disord 2005;7
(6)
570- 576
PubMedGoogle Scholar 126.Chang
KBarnea-Goraly
NKarchemskiy
ASimeonova
DIBarnes
PKetter
TReiss
AL Cortical magnetic resonance imaging findings in familial pediatric bipolar disorder.
Biol Psychiatry 2005;58
(3)
197- 203
PubMedGoogle Scholar 127.Frazier
JABreeze
JLMakris
NGiuliano
ASHerbert
MRSeidman
LBiederman
JHodge
SMDieterich
MEGerstein
EDKennedy
DNRauch
SLCohen
BMCaviness
VS Cortical gray matter differences identified by structural magnetic resonance imaging in pediatric bipolar disorder.
Bipolar Disord 2005;7
(6)
555- 569
PubMedGoogle Scholar 128.Mills
NPDelbello
MPAdler
CMStrakowski
SM MRI analysis of cerebellar vermal abnormalities in bipolar disorder.
Am J Psychiatry 2005;162
(8)
1530- 1532
PubMedGoogle Scholar 129.Pariante
CMDazzan
PDanese
AMorgan
KDBrudaglio
FMorgan
CFearon
POrr
KHutchinson
GPantelis
CVelakoulis
DJones
PBLeff
JMurray
RM Increased pituitary volume in antipsychotic-free and antipsychotic-treated patients of the AEsop first-onset psychosis study.
Neuropsychopharmacology 2005;30
(10)
1923- 1931
PubMedGoogle Scholar 130.Sanches
MSassi
RBAxelson
DNicoletti
MBrambilla
PHatch
JPKeshavan
MSRyan
NDBirmaher
BSoares
JC Subgenual prefrontal cortex of child and adolescent bipolar patients: a morphometric magnetic resonance imaging study.
Psychiatry Res 2005;138
(1)
43- 49
PubMedGoogle Scholar 131.Sanches
MRoberts
RLSassi
RBAxelson
DNicoletti
MBrambilla
PHatch
JPKeshavan
MSRyan
NDBirmaher
BSoares
JC Developmental abnormalities in striatum in young bipolar patients: a preliminary study.
Bipolar Disord 2005;7
(2)
153- 158
PubMedGoogle Scholar 132.Strasser
HCLilyestrom
JAshby
ERHoneycutt
NASchretlen
DJPulver
AEHopkins
RODepaulo
JRPotash
JBSchweizer
BYates
KOKurian
EBarta
PEPearlson
GD Hippocampal and ventricular volumes in psychotic and nonpsychotic bipolar patients compared with schizophrenia patients and community control subjects: a pilot study.
Biol Psychiatry 2005;57
(6)
633- 639
PubMedGoogle Scholar 133.Atmaca
MYildirim
HOzdemir
HPoyraz
AKTezcan
EOgur
E Hippocampal 1H MRS in first-episode bipolar I patients.
Prog Neuropsychopharmacol Biol Psychiatry 2006;30
(7)
1235- 1239
PubMedGoogle Scholar 134.Blumberg
HPKrystal
JHBansal
RMartin
ADziura
JDurkin
KMartin
LGerard
ECharney
DSPeterson
BS Age, rapid-cycling, and pharmacotherapy effects on ventral prefrontal cortex in bipolar disorder: a cross-sectional study.
Biol Psychiatry 2006;59
(7)
611- 618
PubMedGoogle Scholar 135.Coyle
TRKochunov
PPatel
RDNery
FGLancaster
JLMangin
JFRiviere
DPillow
DRDavis
GJNicoletti
MASerap Monkul
EFox
PTSoares
JC Cortical sulci and bipolar disorder.
Neuroreport 2006;17
(16)
1739- 1742
PubMedGoogle Scholar 136.El-Badri
SMCousins
DAParker
SAshton
HCMcAllister
VLFerrier
INMoore
PB Magnetic resonance imaging abnormalities in young euthymic patients with bipolar affective disorder.
Br J Psychiatry 2006;18981- 82
PubMedGoogle Scholar 137.Hwang
JLyoo
IKDager
SRFriedman
SDOh
JSLee
JYKim
SJDunner
DLRenshaw
PF Basal ganglia shape alterations in bipolar disorder.
Am J Psychiatry 2006;163
(2)
276- 285
PubMedGoogle Scholar 138.McDonald
CMarshall
NSham
PCBullmore
ETSchulze
KChapple
BBramon
EFilbey
FQuraishi
SWalshe
MMurray
RM Regional brain morphometry in patients with schizophrenia or bipolar disorder and their unaffected relatives.
Am J Psychiatry 2006;163
(3)
478- 487
PubMedGoogle Scholar 139.Monkul
ESNicoletti
MASpence
DSassi
RBAxelson
DBrambilla
PHatch
JPKeshavan
MRyan
NBirmaher
BSoares
JC MRI study of thalamus volumes in juvenile patients with bipolar disorder.
Depress Anxiety 2006;23
(6)
347- 352
PubMedGoogle Scholar 140.Pardo
PJGeorgopoulos
APKenny
JTStuve
TAFindling
RLSchulz
SC Classification of adolescent psychotic disorders using linear discriminant analysis.
Schizophr Res 2006;87
(1-3)
297- 306
PubMedGoogle Scholar 141.Velakoulis
DWood
SJWong
MTMcGorry
PDYung
APhillips
LSmith
DBrewer
WProffitt
TDesmond
PPantelis
C Hippocampal and amygdala volumes according to psychosis stage and diagnosis: a magnetic resonance imaging study of chronic schizophrenia, first-episode psychosis, and ultra-high-risk individuals.
Arch Gen Psychiatry 2006;63
(2)
139- 149
PubMedGoogle Scholar 142.Voelbel
GTBates
MEBuckman
JFPandina
GHendren
RL Caudate nucleus volume and cognitive performance: are they related in childhood psychopathology?
Biol Psychiatry 2006;60
(9)
942- 950
PubMedGoogle Scholar 143.Yasar
ASMonkul
ESSassi
RBAxelson
DBrambilla
PNicoletti
MAHatch
JPKeshavan
MRyan
NBirmaher
BSoares
JC MRI study of corpus callosum in children and adolescents with bipolar disorder.
Psychiatry Res 2006;146
(1)
83- 85
PubMedGoogle Scholar 144.Ahn
MSBreeze
JLMakris
NKennedy
DNHodge
SMHerbert
MRSeidman
LJBiederman
JCaviness
VSFrazier
JA Anatomic brain magnetic resonance imaging of the basal ganglia in pediatric bipolar disorder.
J Affect Disord 2007;104
(1-3)
147- 154
PubMedGoogle Scholar 145.Atmaca
MYildirim
HOzdemir
HOgur
ETezcan
E Hippocampal 1H MRS in patients with bipolar disorder taking valproate versus valproate plus quetiapine.
Psychol Med 2007;37
(1)
121- 129
PubMedGoogle Scholar 146.Atmaca
MOzdemir
HYildirim
H Corpus callosum areas in first-episode patients with bipolar disorder.
Psychol Med 2007;37
(5)
699- 704
PubMedGoogle Scholar 147.Atmaca
MOzdemir
HCetinkaya
SParmaksiz
SBelli
HPoyraz
AKTezcan
EOgur
E Cingulate gyrus volumetry in drug free bipolar patients and patients treated with valproate or valproate and quetiapine.
J Psychiatr Res 2007;41
(10)
821- 827
PubMedGoogle Scholar 148.Bearden
CEThompson
PMDutton
RAFrey
BNPeluso
MANicoletti
MDierschke
NHayashi
KMKlunder
ADGlahn
DCBrambilla
PSassi
RBMallinger
AGSoares
JC Three-dimensional mapping of hippocampal anatomy in unmedicated and lithium-treated patients with bipolar disorder [published online ahead of print August 8, 2007].
Neuropsychopharmacology 2008;33
(6)
1229- 123810.1038/sj.npp.1301507
PubMedGoogle Scholar 149.Chiu
SWidjaja
FBates
MEVoelbel
GTPandina
GMarble
JBlank
JADay
JBrule
NHendren
RL Anterior cingulate volume in pediatric bipolar disorder and autism [published online ahead of print June 13, 2007].
J Affect Disord 2008;105
(1-3)
93- 99
PubMed10.1016/j.jad.2007.04.019
Google Scholar 150.Kim
MJLyoo
IKDager
SRFriedman
SDChey
JHwang
JLee
YJDunner
DLRenshaw
PF The occurrence of cavum septi pellucidi enlargement is increased in bipolar disorder patients.
Bipolar Disord 2007;9
(3)
274- 280
PubMedGoogle Scholar 151.Molina
VSanchez
JSanz
JReig
SBenito
CLeal
ISarramea
FRebolledo
RPalomo
TDesco
M Dorsolateral prefrontal
N-acetyl-aspartate concentration in male patients with chronic schizophrenia and with chronic bipolar disorder.
Eur Psychiatry 2007;22
(8)
505- 512
PubMedGoogle Scholar 152.Najt
PNicoletti
MChen
HHHatch
JPCaetano
SCSassi
RBAxelson
DBrambilla
PKeshavan
MSRyan
NDBirmaher
BSoares
JC Anatomical measurements of the orbitofrontal cortex in child and adolescent patients with bipolar disorder.
Neurosci Lett 2007;413
(3)
183- 186
PubMedGoogle Scholar 153.Rosso
IMKillgore
WDCintron
CMGruber
SATohen
MYurgelun-Todd
DA Reduced amygdala volumes in first-episode bipolar disorder and correlation with cerebral white matter.
Biol Psychiatry 2007;61
(6)
743- 749
PubMedGoogle Scholar 154.Salisbury
DFKuroki
NKasai
KShenton
MEMcCarley
RW Progressive and interrelated functional and structural evidence of post-onset brain reduction in schizophrenia.
Arch Gen Psychiatry 2007;64
(5)
521- 529
PubMedGoogle Scholar 155.Yucel
KTaylor
VHMcKinnon
MCMacdonald
KAlda
MYoung
LTMacQueen
GM Bilateral hippocampal volume increase in patients with bipolar disorder and short-term lithium treatment [published online ahead of print April 4, 2007].
Neuropsychopharmacology 2008;33
(2)
361- 367
PubMed10.1038/sj.npp.1301405
Google Scholar 156.Yucel
KMcKinnon
MCTaylor
VHMacdonald
KAlda
MYoung
LTMacQueen
GM Bilateral hippocampal volume increases after long-term lithium treatment in patients with bipolar disorder: a longitudinal MRI study.
Psychopharmacology (Berl) 2007;195
(3)
357- 367
PubMedGoogle Scholar 157.Baldwin
RC Is vascular depression a distinct sub-type of depressive disorder? A review of causal evidence.
Int J Geriatr Psychiatry 2005;20
(1)
1- 11
PubMedGoogle Scholar 158.de Groot
JCde Leeuw
FEOudkerk
MHofman
AJolles
JBreteler
MM Cerebral white matter lesions and depressive symptoms in elderly adults.
Arch Gen Psychiatry 2000;57
(11)
1071- 1076
PubMedGoogle Scholar 159.Berlin
JASantanna
JSchmid
CHSzczech
LAFeldman
HI Individual patient- versus group-level data meta-regressions for the investigation of treatment effect modifiers: ecological bias rears its ugly head.
Stat Med 2002;21
(3)
371- 387
PubMedGoogle Scholar 160.Harrison
PJWeinberger
DR Schizophrenia genes, gene expression, and neuropathology: on the matter of their convergence.
Mol Psychiatry 2005;10
(1)
40- 68
PubMedGoogle Scholar 162.Gootjes
LTeipel
SJZebuhr
YSchwarz
RLeinsinger
GScheltens
PMoller
HJHampel
H Regional distribution of white matter hyperintensities in vascular dementia, Alzheimer's disease and healthy aging.
Dement Geriatr Cogn Disord 2004;18
(2)
180- 188
PubMedGoogle Scholar 163.Thomas
AJPerry
RBarber
RKalaria
RNO'Brien
JT Pathologies and pathological mechanisms for white matter hyperintensities in depression.
Ann N Y Acad Sci 2002;977333- 339
PubMedGoogle Scholar 164.Shenton
MEDickey
CCFrumin
MMcCarley
RW A review of MRI findings in schizophrenia.
Schizophr Res 2001;49
(1-2)
1- 52
PubMedGoogle Scholar 165.McCarley
RWWible
CGFrumin
MHirayasu
YLevitt
JJFischer
IAShenton
ME MRI anatomy of schizophrenia.
Biol Psychiatry 1999;45
(9)
1099- 1119
PubMedGoogle Scholar 166.Campbell
SMarriott
MNahmias
CMacQueen
GM Lower hippocampal volume in patients suffering from depression: a meta-analysis.
Am J Psychiatry 2004;161
(4)
598- 607
PubMedGoogle Scholar 167.Videbech
PRavnkilde
B Hippocampal volume and depression: a meta-analysis of MRI studies.
Am J Psychiatry 2004;161
(11)
1957- 1966
PubMedGoogle Scholar 168.Jiang
HGuo
WLiang
XRao
Y Both the establishment and the maintenance of neuronal polarity require active mechanisms: critical roles of GSK-3beta and its upstream regulators.
Cell 2005;120
(1)
123- 135
PubMedGoogle Scholar 169.Regier
DAFarmer
MERae
DSLocke
BZKeith
SJJudd
LLGoodwin
FK Comorbidity of mental disorders with alcohol and other drug abuse: results from the Epidemiologic Catchment Area (ECA) Study.
JAMA 1990;264
(19)
2511- 2518
PubMedGoogle Scholar 170.Rosenbloom
MSullivan
EVPfefferbaum
A Using magnetic resonance imaging and diffusion tensor imaging to assess brain damage in alcoholics.
Alcohol Res Health 2003;27
(2)
146- 152
PubMedGoogle Scholar