Structural Neuroimaging Studies in Major Depressive Disorder: Meta-analysis and Comparison With Bipolar Disorder | Depressive Disorders | JAMA Psychiatry | JAMA Network
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Figure 1. 
Continuous variables evaluated in major depressive disorder (MDD) meta-analysis. Hedges g (Cohen effect size with small sample correction) is shown for each structure, with 95% confidence intervals. The effect size is positive when the structure is larger in patients with MDD compared with controls and negative when the structure is smaller in MDD patients. The number of studies included in each meta-analysis is indicated with each structure. CSF indicates cerebrospinal fluid; CT, computed tomography; DWMH, deep white matter hyperintensities; GM, gray matter; L, left; MRI, magnetic resonance imaging; PFC, prefrontal cortex; PVH, periventricular hyperintensities; R, right; ScGMH, subcortical gray matter hyperintensities; and WM, white matter.

Continuous variables evaluated in major depressive disorder (MDD) meta-analysis. Hedges g (Cohen effect size with small sample correction) is shown for each structure, with 95% confidence intervals. The effect size is positive when the structure is larger in patients with MDD compared with controls and negative when the structure is smaller in MDD patients. The number of studies included in each meta-analysis is indicated with each structure. CSF indicates cerebrospinal fluid; CT, computed tomography; DWMH, deep white matter hyperintensities; GM, gray matter; L, left; MRI, magnetic resonance imaging; PFC, prefrontal cortex; PVH, periventricular hyperintensities; R, right; ScGMH, subcortical gray matter hyperintensities; and WM, white matter.

Figure 2. 
Categorical variables evaluated in major depressive disorder (MDD) meta-analysis. Odds ratio is shown for each type of hyperintensity, with 95% confidence intervals. Odds ratio larger than 1 indicates that the hyperintensity is more common in patients with MDD compared with control group members. The number of studies included in each meta-analysis is indicated with each type of hyperintensity. GM indicates gray matter; MRI, magnetic resonance imaging; and WM, white matter.

Categorical variables evaluated in major depressive disorder (MDD) meta-analysis. Odds ratio is shown for each type of hyperintensity, with 95% confidence intervals. Odds ratio larger than 1 indicates that the hyperintensity is more common in patients with MDD compared with control group members. The number of studies included in each meta-analysis is indicated with each type of hyperintensity. GM indicates gray matter; MRI, magnetic resonance imaging; and WM, white matter.

Table 1. Patient and Control Demographic and Clinical Data Recorded in the Database
Patient and Control Demographic and Clinical Data Recorded in the Database
Table 2. List of Studies Included in the MDD Meta-analysis
List of Studies Included in the MDD Meta-analysis
Table 3. Meta-analysis of Continuous Data Comparing Patients With MDD vs Controlsa
Meta-analysis of Continuous Data Comparing Patients With MDD vs Controlsa
Table 4. Meta-analysis of Categorical Data Comparing Patients With MDD vs Controls
Meta-analysis of Categorical Data Comparing Patients With MDD vs Controls
Table 5. Statistical Comparison of the Present MDD Meta-analysis With a Previous Meta-analysis of BD9
Statistical Comparison of the Present MDD Meta-analysis With a Previous Meta-analysis of BD
July 2011

Structural Neuroimaging Studies in Major Depressive Disorder: Meta-analysis and Comparison With Bipolar Disorder

Author Affiliations

Author Affiliations: Section of Neurobiology of Psychosis, Department of Psychosis Studies (Drs Kempton and Frangou and Ms Salvador), Department of Neuroimaging (Drs Kempton, Simmons, and Williams), Institute of Psychiatry, King's College London, and National Institute for Health Research Biomedical Research Centre for Mental Health at the South London and Maudsley National Health Service Foundation Trust (Dr Simmons), London, England; Department of Experimental Psychology, University of Bristol, Bristol, England (Dr Munafò); and Department of Psychiatry, University of Oxford, Oxford, England (Dr Geddes).

Arch Gen Psychiatry. 2011;68(7):675-690. doi:10.1001/archgenpsychiatry.2011.60

Context  Although differences in clinical characteristics exist between major depressive disorder (MDD) and bipolar disorder (BD), consistent structural brain abnormalities that distinguish the disorders have not been identified.

Objectives  To investigate structural brain changes in MDD using meta-analysis of primary studies; assess the effects of medication, demographic, and clinical variables; and compare the findings with those of a meta-analysis of studies on BD.

Data Sources  The MEDLINE, EMBASE, and PsycINFO databases were searched for studies from January 1, 1980, to February 2, 2010.

Study Selection  Two hundred twenty-five studies that used magnetic resonance imaging or x-ray computed tomography to compare brain structure in patients with MDD with that of controls were included in an online database, and 143 that measured common brain structures were selected for meta-analysis.

Data Extraction  Twenty-five variables, including demographic and clinical data, were extracted from each study, when available. For the meta-analysis, mean structure size and standard deviation were extracted for continuous variables, and the proportion of patients and controls with an abnormality in brain structure was extracted for categorical variables.

Data Synthesis  Compared with the structure of a healthy brain, MDD was associated with lateral ventricle enlargement; larger cerebrospinal fluid volume; and smaller volumes of the basal ganglia, thalamus, hippocampus, frontal lobe, orbitofrontal cortex, and gyrus rectus. Patients during depressive episodes had significantly smaller hippocampal volume than patients during remission. Compared with BD patients, those with MDD had reduced rates of deep white matter hyperintensities, increased corpus callosum cross-sectional area, and smaller hippocampus and basal ganglia. Both disorders were associated with increased lateral ventricle volume and increased rates of subcortical gray matter hyperintensities compared with healthy controls.

Conclusions  The meta-analyses revealed structural brain abnormalities in MDD that are distinct from those observed in BD. These findings may aid investigators attempting to discriminate mood disorders using structural magnetic resonance imaging data.

Epidemiologic and treatment studies have confirmed differences in clinical characteristics between major depressive disorder (MDD) and bipolar disorder (BD). Major depressive disorder, compared to BD, has a higher lifetime prevalence (16% vs 2%),1,2 has an older median age at onset (32 vs 25 years),3 and is associated with a lower number of depressive episodes.4 Antidepressants are the most commonly prescribed medication for MDD5; mood stabilizers, such as lithium, are the most frequently prescribed treatment for BD.6 However, despite these differences, consistent biomarkers distinguishing the disorders have been elusive.

It is not clear whether changes in brain structure differentiate MDD from BD or whether common abnormalities are present in both disorders. Reviews7,8 of imaging studies have attempted to identify structural differences specific to each disorder in a qualitative fashion. In addition, meta-analyses, which quantitatively summarize research findings, have revealed evidence of structural abnormalities in patients with BD9-11 and MDD12-16 compared with healthy individuals. However, to our knowledge, meta-analytical methods have not been used to establish whether the identified abnormalities distinguish the disorders.

In the present study, we conducted a comprehensive meta-analysis of studies on brain structural changes in patients with MDD compared with controls and examined the specificity of these abnormalities by comparing the findings with those found in patients with BD. In the meta-analysis of MDD imaging studies, we increased both the number of included publications and brain regions by a factor of 2 compared with the largest previous meta-analysis.13 Using data from a meta-analysis of studies on BD,9 we made direct statistical comparisons between structural measures from each disorder while attempting to control for differences in study characteristics such as scanning parameters and patient demographics. Previous meta-analyses12,13 of studies on patients with MDD most consistently report hippocampal volumetric reduction; we applied meta-regression techniques to investigate the effect of clinical variables on hippocampal volume. Finally, we provide an online database of 225 MDD structural imaging studies, listing 25 variables from each study, when available ( The database is presented as an open-access wiki,17 enabling other researchers to add studies.


The study was divided into 3 parts: (1) the construction of a database of 225 studies investigating structural abnormalities in MDD, (2) a meta-analysis comparing patients with MDD with controls from a subgroup of 143 studies, and (3) a statistical comparison of structural abnormalities between patients with MDD and those with BD.


The inclusion criteria for the database required peer-reviewed studies that measured brain structure using x-ray computed tomography (CT) or magnetic resonance imaging (MRI) in patients with MDD and a control group. The MEDLINE, EMBASE, and PsycINFO databases were searched from January 1, 1980, to February 2, 2010, using a combination of relevant expanded subject headings and free-text searches; detailed search terms are given in our wiki database. A total of 3960 records of unique publications were initially examined; we excluded case studies, reviews, studies that had not used standard diagnostic criteria, studies that combined MDD with BD patients in a single group, duplicate publications, and investigations using voxel-based morphometry (VBM) because the results of these studies cannot be included in a traditional meta-analysis. We did not include epidemiologic studies that diagnosed depression based on cutoffs of rating scales or considered antidepressant use a proxy for MDD diagnosis because the agreement between these measures and standard diagnostic criteria is questionable.18 Two hundred twenty-five publications fulfilled the inclusion criteria and were included in the database.


The following data were recorded from each study when available: number of MDD patients and controls, mean age, number of males and females in the patient and control groups, and the diagnostic classification system used. Mean age of the patient at onset of the disease and mean Hamilton Scale for Depression (HAM-D)19 score were also recorded. The HAM-D scale was chosen over other depression rating scales because it is the most commonly reported. For current medication, we recorded the number of patients who were described as drug free (not necessarily medication naive), as well as the number using mood stabilizers, antipsychotics, and antidepressants, including the class of antidepressant (selective serotonin reuptake inhibitor, tricyclic, monoamine oxidase inhibitor, or other). Information on current medication use was extracted from the publications directly; because data regarding prior exposure to medication were rarely presented, this information was not systematically collected. For each study, we recorded all brain structures or abnormalities measured, whether the measurement was MRI or CT based, slice thickness, and field strength of the MRI scanner.

The majority of variables in the database were not normally distributed; therefore, correlations were assessed using Spearman rank correlation (SPSS 15.0; SPSS Inc, Chicago, Illinois).

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 225 studies. As with previous meta-analyses, exact anatomic definitions of individual structures varied across studies. For a given structure, some studies reported left and right measurements separately, and other studies reported the combined measurement. For studies that reported measures for the left and right volumes but not the total, we determined the mean and standard deviation for the total volume, using a reported method13 that requires an estimate of the correlation coefficient between the left and right volumes. This coefficient was set as 0.8, although it was varied in the sensitivity analysis (described in the “Sensitivity Analysis” subsection of the “Methods” section). To ensure that the meta-analysis was sufficiently powered, brain regions were included if there were 3 or more independent studies that reported a mean and SD in both the control and patient groups (continuous measures), and abnormalities were included if there were 3 or more independent studies that reported the number of patients and controls with the abnormality (categorical measure). From the 225 studies in the database, there were a total of 324 different brain regions examined; however, only 63 regions were examined by 3 or more studies, and these were entered in the meta-analysis. The total number of studies included in the meta-analysis was 143; many studies in the database did not examine 1 or more of the 63 identified regions. The studies in the meta-analysis included 9 publications that used CT measures of total lateral ventricle volume (analyzed as a subgroup), and all other brain structures were examined using MRI.

Combining Study Estimates

For continuous outcome measures, Hedges g was used, which is the Cohen effect size with a correction for bias from small sample sizes.20 The percentage difference effect size is also provided to aid biological interpretation of the data.21 The majority of studies report absolute volume measures; however, some report volumes as ratios of the entire brain or cross-sectional area measures. All such measurements have been included in the meta-analysis; however, because combining measures may increase heterogeneity, an additional analysis was carried out with volume measures only (see “Sensitivity Analysis” subsection of the “Methods” section). For categorical outcome measures, the odds ratio was used. Outcome measures were recorded from each study and were independently checked to ensure accuracy. For a given brain structure, if 2 or more studies by the same research group reported similar patient or control demographics, we contacted the authors of those studies to determine whether there was overlap in the sample and, if this was the case, included only the largest relevant study. A total of 150 measures from 52 studies were excluded for this reason. A meta-analysis for each brain structure was performed (using the metan command in Stata 9.2, 2006; StataCorp LP, College Station, Texas). Outcome measures were combined using a random-effects inverse-weighted variance model.22 Because the meta-analyses examined a large number of regions and are susceptible to type I errors, results that survive Bonferroni correction for multiple comparisons (corrected for 63 regions, P < .0008) are indicated.

Combining Patient Subgroups

A minority of imaging studies presented measures from subgroups of patients. For these studies, we entered the subgroups in the meta-analysis as if they were separate studies and, in each case, the number of individuals in the control group was considered the sample size of the control group divided by the number of patient subgroups. This method has been used in a previous meta-analysis.9 If studies reported on males and females separately, we entered the results as if they were from 2 separate studies, a technique adopted by another previous meta-analysis.23

Assessing Between-Study Heterogeneity

To test for between-study heterogeneity, the Cochran Q test statistic was calculated.24 The I2 statistic, which is equal to the percentage of total variation across studies as the result of heterogeneity, was also calculated to aid interpretability of between-study heterogeneity.25

Small-Study Bias

The effect of small-study bias (which may include publication bias) was investigated for regions in which the pooled effect size revealed a significant group difference between MDD patients and controls and when at least 5 studies were included in the meta-analysis to ensure that the test was sufficiently powered. Small-study bias was assessed using the Egger regression test.26

Effect of Clinical Variables on Hippocampal Volume

The number of brain regions and clinical variables included in the database allows a potentially high number of correlations to be examined, which may lead to type I errors. Thus, the analysis was limited to the effect of clinical variables on total hippocampal volume. We selected this region because of the robust evidence of volumetric reduction in MDD12,13 and because many studies have measured this structure, ensuring adequate statistical power. A random effects meta-regression was implemented (metareg command in Stata 9.2) to examine age at onset, HAM-D score, percentage of patients using antidepressants, number of depressive episodes, and patient age. In addition, we combined studies that directly compared the same MDD subgroups (eg, depressed vs remitted patients) in a supplementary meta-analysis. Finally, we determined whether the reduction in hippocampal volume remained when the meta-analysis was restricted to the following MDD patient groups: no comorbid anxiety disorders, no history of alcohol/substance abuse or dependence, first episode, adolescents, adults, and elderly patients.

Sensitivity Analysis

To test how robust the results were to variations in the meta-analysis method, the effect of the following was examined: (1) percentage difference in the patient and control mean volumes as an outcome measure for continuous data (calculation of this effect size and the effect size variance has been described in more detail in previous meta-analytical studies21,23); (2) excluding studies that reported continuous data as areas, lengths, or ratios rather than absolute volume; and (3) setting the correlation coefficient between the left and right regional volumes as 0.1, 0.5, and 1.

Stratified Meta-analysis

Two meta-analytical approaches may be taken to examine differences between MDD and BD: (1) meta-analysis of studies directly comparing the same brain structure in patients with MDD vs those with BD or (2) indirect analysis comparing the pooled effect size from studies comparing MDD patients vs controls with that from studies comparing BD patients vs controls. We adopted the second approach, which has the advantage of including more studies and brain structures because there are very few direct comparisons. Thus, to compare the results from the MDD meta-analysis with those of a previous meta-analysis of BD studies,9 we combined the effect sizes from BD patients vs controls with MDD patients vs controls (effectively, a meta-analysis of studies on patients with affective disorder) and performed a stratified meta-analysis using a z test to compare across the 2 disorders. To match the method exactly, the BD meta-analysis was reanalyzed, using the technique of combining left and right brain structural measures adopted in the present MDD meta-analysis. To reduce the number of comparisons, we focused on brain regions that were significantly different from those of controls (before Bonferroni correction) in either the MDD or BD meta-analysis.

Verifying Diagnostic Differences

We adopted a 2-stage process to ensure that significant differences between the groups that were identified in the stratified meta-analysis were not the result of variations in patient demographics or scanning parameters rather than MDD or BD diagnosis. First, for each brain structure, we examined whether the following summary variables were significantly different between MDD and BD studies, using an independent-samples t test: percentage of patients who were female, medication free, using antidepressants, using neuroleptics, and using mood stabilizers; mean patient age; scanner magnetic field strength; and slice thickness. Second, when a significant difference was identified between BD and MDD studies for those variables, a meta-regression was performed separately in MDD and BD studies to examine the effect of the variable on the given brain structure.


Demographic and clinical data from the database are reported, followed by results from the MDD meta-analyses and comparison with the BD meta-analysis.


Details from 225 studies that included a total of 9533 patients with MDD and 8846 controls were entered into the database. Table 1 summarizes the variables recorded. The classification systems used for defining MDD were DSM-IV (156 studies); DSM-III-R (46 studies); DSM-III (13 studies); International Classification of Diseases, Ninth Revision or International Classification of Diseases, Tenth Revision (5 studies); Research Diagnostic Criteria27 (4 studies); and Chinese Classification of Mental Disorders28 (1 study). Two hundred eight studies used MRI and 17 studies used CT imaging. Among MRI studies, 81% used a 1.5-T scanner; 11% used a lower field strength and 6% used a higher field strength. The mean (SD) slice thickness was 9.0 (1.0) mm in CT studies and 2.8 (2.1) mm in MRI studies. There was evidence that studies were recruiting larger numbers of patients over time (R = 0.21; P = .002) and that the number of studies per year was increasing (R = 0.94; P < .001).


The 143 studies included in the meta-analysis29-171 are listed in Table 2. Compared with controls, patients with MDD had larger lateral ventricular and cerebrospinal fluid (CSF) volumes and smaller volumes of the total caudate, putamen, globus pallidus, thalamus, hippocampus, frontal lobe, orbitofrontal gray matter, and gyrus rectus (Figure 1 and Table 3). The pituitary gland was increased in volume, with borderline significance (P = .054). Patients with MDD had moderately increased rates of MRI signal hyperintensities, but this was dependent on the measurement technique used. When hyperintensity rating scales were used, periventricular hyperintensities were increased (Figure 1 and Table 3); when categorical classification was used (patient classified as having hyperintensities or no hyperintensities), subcortical gray matter hyperintensities were increased (Figure 2 and Table 4). The differences that survived Bonferroni correction (P < .0008) were increased lateral ventricle and CSF volume and decreased hippocampal and gyrus rectus volume (Table 3). No small-study bias was detected in any of the brain regions identified in this paragraph (all P > .11).

Effect of Clinical Variables on Hippocampal Volume

Because outliers may have a disproportionate effect on meta-regression analysis, 2 outliers113,167 were removed before investigating the effect of clinical variables on total hippocampal volume (effect sizes of outliers, −3.8 and −2.2; effect size range of the remaining 35 studies,  −1.3 to +0.4; and pooled effect size after 2 studies were excluded,  −0.40). There was no significant effect of age at onset (23 studies, P = .75), HAM-D score (18 studies, P = .58), percentage of patients using antidepressants (22 studies, P = .61), number of depressive episodes (20 studies, P = .25), or patient age (35 studies, P = .54) on the difference in hippocampal volume between patients and controls. For each meta-regression performed, the heterogeneity of the included studies remained significant (I2 > 28%). In terms of subgroups, patients with MDD in remission had a significantly larger hippocampal volume compared with patients who were currently depressed (4 studies, effect size,  0.34; 95% confidence interval,  0.02-0.67; P = .04); there was no significant difference in volume between patients with remitted MDD and controls (5 studies, P = .25). In addition, there was no significant difference in hippocampal volume between patients with first episode vs multiple episodes (4 studies, P = .32) or patients with early- vs late-onset depression (3 studies, P = .24). The significant reduction in hippocampal volume remained when the meta-analysis was limited to first-episode studies (6 studies; effect size,  −0.22; P = .04); studies that excluded patients with a comorbid anxiety disorder (10 studies; effect size,  −0.39; P < .001); and studies that excluded patients with a history of drug abuse/dependence or substance abuse/dependence (6 studies; effect size,  −0.32; P < .001), adults (mean age, 20-60 years; 22 studies; effect size,  −0.43; P < .001), and elderly patients (60-75 years; 8 studies; effect size,  −0.34; P < .001). Although the effect size was similar to that of other subgroups, there was no significant reduction of hippocampal volume in adolescent patients (12-19 years; 5 studies; effect size,  −0.35; P = .11).

Sensitivity Analysis

When percentage change was used as the effect size for continuous data, there was a reduction in the volume of the right orbitofrontal cortex and an increase in deep white matter hyperintensities as measured by rating scales in patients with MDD, and the decrease previously observed in the globus pallidus became a trend. If ratio, length, and area measurements were excluded, lateral, ventricle measures using CT imaging could not be included because all studies reported a ventricle to brain ratio measure; no other changes were noted. There was no change in the results when the correlation coefficient between left and right regions was set to 0, 0.5, or 1.

Stratified Meta-analysis

Nine regions included in both the MDD and BD meta-analyses showed significant differences from controls, allowing a comparison to be made (Table 5). Six of these regions differed between MDD and BD. Compared with BD patients or controls, patients with MDD had significantly reduced volumes of the caudate, putamen, globus pallidus, and hippocampus. Compared with MDD patients or controls, patients with BD had significantly reduced cross-sectional areas of the corpus callosum and increased rates of deep white matter hyperintensities. Compared with controls, both MDD and BD patients showed ventricular dilation and increased rates of subcortical gray matter hyperintensities, but there was no significant difference between the patient groups.

Verifying Diagnostic Differences

When we examined differences in study characteristics between BD and MDD investigations for each of the 6 brain regions identified in the previous paragraph, there were significant differences in terms of patient age (5 regions), sex (3 regions), and medication use (5 regions), but no significant differences in terms of scanner field strength or slice thickness. From the follow-up meta-regression analyses, there was no significant effect of patient age, sex, or medication use on any of the 6 regions in either BD or MDD studies. One exception was patient age in BD studies, in which increased age was associated with increased hippocampal volume compared with matched controls (P = .001).


In patients with MDD compared with controls, we identified increased lateral ventricle and CSF volume; reduced volume of the basal ganglia, thalamus, hippocampus, frontal lobe, orbitofrontal cortex, and gyrus rectus; and increased rates of periventricular and subcortical gray matter hyperintensities. Currently depressed MDD patients had smaller hippocampal volumes vs those with remitted MDD. Within mood disorders, basal ganglia and hippocampal volume reductions appear to be specific to MDD, and reduced corpus callosum cross-sectional area and increased rates of deep white matter hyperintensities appear to be specific to BD.


In MDD patients compared with controls, we found bilateral reductions in hippocampal volume (−5%) as reported by previous meta-analyses,12,13,174 and also showed a strong significant trend for increased pituitary volume (P = .054, 5%). These findings may provide evidence for the involvement of the hypothalamic-pituitary-adrenal axis in MDD. The anterior pituitary produces adrenocorticotropic hormone, and it is conceivable that an increased volume of the pituitary may be associated with increased adrenocorticotropic hormone production. A primary role of adrenocorticotropic hormone is stimulation of the adrenal cortex, which responds by producing glucocorticoids. There is strong evidence that glucocorticoid levels are increased in MDD, and prolonged high levels may damage hippocampal neurons.175 A small number of studies measured the adrenal gland in MDD; these were not included in our meta-analysis because only 2 (not meeting our criterion of 3) measured this structure in a group including only patients with MDD. However, because of the weight of evidence supporting the involvement of the hypothalamic-pituitary-adrenal axis, we entered these studies into an additional meta-analysis (not previously reported). The 3 case-control studies comprised 1 MRI study that evaluated 3 BD patients with 32 MDD patients as a single group176 and 2 CT studies of MDD patients.177,178 Adrenal volume was significantly increased compared with controls (effect size,  0.81; 95% confidence interval,  0.45-1.16; P < .0001), providing further evidence of an association between MDD and hypothalamic-pituitary-adrenal axis abnormalities. The finding that hippocampal volume was significantly smaller in patients with a depressed compared with remitted state raises the possibility that reductions in hippocampal volume may normalize during remission, and it is tempting to speculate that this may be the result of neurogenesis in the dentate gyrus.179 A longitudinal study performing scans on the same patients during depression and after recovery would be an effective way of examining these putative changes in more detail. Although we did not detect an association between antidepressant use and hippocampal volume, there is growing evidence that antidepressants may upregulate neurogenesis. Malberg et al180 reported that chronic antidepressant therapy increased neurogenesis in rats and an antipsychotic medication had no effect. More recently, a postmortem study181 reported that depressed patients who had been treated with antidepressants had an increased number of neural progenitor cells in the dentate gyrus compared with untreated patients or controls. In previous meta-analyses, Videbech and Ravnkilde12 reported that increased duration of illness was associated with smaller right hippocampal volume, and McKinnon et al14 did not find hippocampal reduction in first episodes of MDD. Conversely, we did not find an association with duration of illness and determined that hippocampal volume reduction was present in patients at first episode. However, in our analysis, the effect size in first-episode studies (0.22) was numerically less than that in all studies combined (0.47), suggesting that the volumetric reduction may be less marked in the early stages of the illness.


Four frontal regions were smaller in MDD patients compared with controls. Although we could not confirm that the subgenual prefrontal cortex was reduced in volume in MDD, it is possible that functional,59,182 rather than structural, abnormalities are of greater prominence in this region. The most significant effect sizes were observed in the orbitofrontal cortex and gyrus rectus. Deficits of prefrontal cortical activation in MDD are relatively consistent in functional neuroimaging studies,183 and postmortem studies184 have reported reduced neuronal and glial density in the dorsal lateral and orbitofrontal cortex. We confirm the findings of a previous meta-analysis,13 which reported reduced volume of the caudate and putamen in patients with MDD, but we also found significant volume reductions of the globus pallidus. Although the basal ganglia have primarily been linked to motor function, the ventral striatum, including the nucleus accumbens, has been strongly associated with limbic systems, particularly reward networks.185


Although increased rates of hyperintensities are considered an established finding in MDD, particularly in older patients,186 our meta-analysis showed only modest increases compared with healthy controls. The reported effect sizes in this meta-analysis were smaller than those in 2 earlier meta-analyses16,187 of depression studies. Our meta-analysis included only studies on patients with MDD; however, both previous meta-analyses included studies that evaluated MDD and BD patients as a single group,188-190 which may have inflated the effect size. Indeed, our study indicates that patients with BD have more than a 2-fold increase in rates of deep white matter hyperintensities compared with MDD patients (Table 5).


We excluded reports on VBM from the meta-analysis; however, a qualitative review of these studies partially supports our findings. In 12 VBM studies that examined gray matter volume, the most consistent findings were hippocampal volume reduction (7 studies)191-197 and volumetric reductions within the frontal lobe (7 studies).191,192,194-196,198,199 Changes shown with VBM that were not found in our meta-analysis included reductions within the amygdala (4 studies)194-196,200 and cingulate cortex (6 studies),193-195,199-201 although 1 study198 found an increase in volume.


Based on findings from a large number of independent studies in the meta-analysis comparison, MDD is associated with reductions in basal ganglia and hippocampal volume, and BD is more strongly associated with white matter abnormalities, specifically deep white matter hyperintensities and reduced corpus callosum area. In terms of similarities, both disorders showed ventricular enlargement and increased rates of subcortical gray matter hyperintensities. The positive association between patient age and hippocampal volume in the BD sample reinforces the identified difference in hippocampal volume between patients with MDD and those with BD; if studies on BD had recruited older patients in the same way as MDD studies did, the difference would have been greater. The larger extent of gray matter volume reductions in MDD was surprising, given that BD is considered a more chronic illness and is associated with an earlier age at onset2 and more episodes of major depression compared with MDD.4 The finding that white matter abnormalities were more strongly associated with BD than MDD was also unexpected; however, this is supported in a review by Mahon et al,202 who reported evidence of abnormal white matter in BD from studies using a variety of neuroimaging techniques in addition to neuropathologic and genetic studies. Because studies of twins have shown that there are both overlapping and distinct genetic risk factors for BD and MDD,203 it is possible that the unique genetic factors for each disorder are associated with the distinct structural abnormalities identified in this meta-analysis. Twin studies have also shown that environmental factors have a stronger influence in MDD than in BD.204 It is possible that the reduction in hippocampal volume observed in MDD but not BD is linked to stressful life events playing a more prominent role in the development of MDD.


Although the case-control meta-analysis is statistically highly powered, the meta-regression analysis of clinical variables lacks power and may be prone to type I and type II errors. In the comparison between MDD and BD, we limited the analysis to regions that were significantly different from controls in either the MDD or BD meta-analysis. This strategy reduces the number of comparisons and associated type I errors; however, it is possible that brain regions that distinguish the disorders were overlooked. Although we attempted to take into account differences in medications between the groups, it was not possible to account for this entirely because of the limited information reported in studies. A previous meta-analysis of BD studies9 and other studies205,206 have shown that use of lithium may increase gray matter volume, and it is possible that lithium may be masking abnormalities that would have been observed if patients with BD were not using this medication. The results from a long-term prospective study207 suggest an approximate 1% conversion rate from MDD to BD every year, which complicates the comparison of MDD and BD studies. Therefore, it is possible that the differences in brain structure between MDD patients who do not convert and BD patients are more pronounced than the differences reported in this study.

In conclusion, in this meta-analysis, we have shown robust structural brain abnormalities in MDD and particular changes in brain volume that may distinguish MDD from BD. These results may aid imaging studies aiming to use structural MRI data to distinguish patients with MDD from those with BD. Further studies may reveal whether these abnormalities are a risk factor for developing MDD, when they first occur, and whether they are predictive of treatment response.

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Article Information

Correspondence: Matthew J. Kempton, MSc, PhD, Department of Neuroimaging, PO89, Institute of Psychiatry, King's College London, De Crespigny Park, London SE5 8AF, England (

Submitted for Publication: April 22, 2010; final revision received September 28, 2010; accepted November 30, 2010.

Author Contributions: Dr Kempton had full access to all 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: The authors acknowledge financial support from the National Institute for Health Research (NIHR) Specialist Biomedical Research Centre for Mental Health award to the South London and Maudsley NHS Foundation Trust and the Institute of Psychiatry, King's College London, and the ongoing support of the Wellcome Trust and Engineering and Physical Sciences Research Council toward the Medical Engineering Centre within King's College London. Dr Kempton was also supported by a Wellcome Trust Value in People Award.

Role of Sponsors: The sponsors of the study had no role in the design or conduct of this study; in the collection, management, analysis, and interpretation of the data; or in the preparation, review, or approval of the manuscript.

Additional Contributions: Magaly Cermeño, BEng, independently verified data entered into the meta-analysis.

Kessler  RC, Berglund  P, Demler  O, Jin  R, Koretz  D, Merikangas  KR, Rush  AJ, Walters  EE, Wang  PS, National Comorbidity Survey Replication.  The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R).  JAMA. 2003;289(23):3095-3105. PubMedGoogle ScholarCrossref
Merikangas  KR, Akiskal  HS, Angst  J, Greenberg  PE, Hirschfeld  RM, Petukhova  M, Kessler  RC.  Lifetime and 12-month prevalence of bipolar spectrum disorder in the National Comorbidity Survey replication.  Arch Gen Psychiatry. 2007;64(5):543-552. PubMedGoogle ScholarCrossref
Kessler  RC, Berglund  P, Demler  O, Jin  R, Merikangas  KR, Walters  EE.  Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication.  Arch Gen Psychiatry. 2005;62(6):593-602. PubMedGoogle ScholarCrossref
Perlis  RH, Brown  E, Baker  RW, Nierenberg  AA.  Clinical features of bipolar depression versus major depressive disorder in large multicenter trials.  Am J Psychiatry. 2006;163(2):225-231. PubMedGoogle ScholarCrossref
Olfson  M, Marcus  SC, Druss  B, Elinson  L, Tanielian  T, Pincus  HA.  National trends in the outpatient treatment of depression.  JAMA. 2002;287(2):203-209. PubMedGoogle ScholarCrossref
Blanco  C, Laje  G, Olfson  M, Marcus  SC, Pincus  HA.  Trends in the treatment of bipolar disorder by outpatient psychiatrists.  Am J Psychiatry. 2002;159(6):1005-1010. PubMedGoogle ScholarCrossref
Savitz  J, Drevets  WC.  Bipolar and major depressive disorder: neuroimaging the developmental-degenerative divide.  Neurosci Biobehav Rev. 2009;33(5):699-771. PubMedGoogle ScholarCrossref
Konarski  JZ, McIntyre  RS, Kennedy  SH, Rafi-Tari  S, Soczynska  JK, Ketter  TA.  Volumetric neuroimaging investigations in mood disorders: bipolar disorder versus major depressive disorder.  Bipolar Disord. 2008;10(1):1-37. PubMedGoogle ScholarCrossref
Kempton  MJ, Geddes  JR, Ettinger  U, Williams  SC, Grasby  PM.  Meta-analysis, database, and meta-regression of 98 structural imaging studies in bipolar disorder.  Arch Gen Psychiatry. 2008;65(9):1017-1032. PubMedGoogle ScholarCrossref
Arnone  D, Cavanagh  J, Gerber  D, Lawrie  SM, Ebmeier  KP, McIntosh  AM.  Magnetic resonance imaging studies in bipolar disorder and schizophrenia: meta-analysis.  Br J Psychiatry. 2009;195(3):194-201. PubMedGoogle ScholarCrossref
Vita  A, De Peri  L, Sacchetti  E.  Gray matter, white matter, brain, and intracranial volumes in first-episode bipolar disorder: a meta-analysis of magnetic resonance imaging studies.  Bipolar Disord. 2009;11(8):807-814. PubMedGoogle ScholarCrossref
Videbech  P, Ravnkilde  B.  Hippocampal volume and depression: a meta-analysis of MRI studies.  Am J Psychiatry. 2004;161(11):1957-1966. PubMedGoogle ScholarCrossref
Koolschijn  PC, van Haren  NE, Lensvelt-Mulders  GJ, Hulshoff Pol  HE, Kahn  RS.  Brain volume abnormalities in major depressive disorder: a meta-analysis of magnetic resonance imaging studies.  Hum Brain Mapp. 2009;30(11):3719-3735. PubMedGoogle ScholarCrossref
McKinnon  MC, Yucel  K, Nazarov  A, MacQueen  GM.  A meta-analysis examining clinical predictors of hippocampal volume in patients with major depressive disorder.  J Psychiatry Neurosci. 2009;34(1):41-54. PubMedGoogle Scholar
Hamilton  JP, Siemer  M, Gotlib  IH.  Amygdala volume in major depressive disorder: a meta-analysis of magnetic resonance imaging studies.  Mol Psychiatry. 2008;13(11):993-1000. PubMedGoogle ScholarCrossref
Videbech  P.  MRI findings in patients with affective disorder: a meta-analysis.  Acta Psychiatr Scand. 1997;96(3):157-168. PubMedGoogle ScholarCrossref
Hoffmann  R.  A wiki for the life sciences where authorship matters.  Nat Genet. 2008;40(9):1047-1051. PubMedGoogle ScholarCrossref
Eaton  WW, Neufeld  K, Chen  LS, Cai  G.  A comparison of self-report and clinical diagnostic interviews for depression: diagnostic interview schedule and schedules for clinical assessment in neuropsychiatry in the Baltimore epidemiologic catchment area follow-up.  Arch Gen Psychiatry. 2000;57(3):217-222. PubMedGoogle ScholarCrossref
Hamilton  M.  A rating scale for depression.  J Neurol Neurosurg Psychiatry. 1960;23:56-62. PubMedGoogle ScholarCrossref
Hedges  LV, Olkin  I.  Statistical Methods for Meta-analysis. Orlando, FL: Academic Press; 1985
McDonald  C, Zanelli  J, Rabe-Hesketh  S, Ellison-Wright  I, Sham  P, Kalidindi  S, Murray  RM, Kennedy  N.  Meta-analysis of magnetic resonance imaging brain morphometry studies in bipolar disorder.  Biol Psychiatry. 2004;56(6):411-417. PubMedGoogle ScholarCrossref
DerSimonian  R, Laird  N.  Meta-analysis in clinical trials.  Control Clin Trials. 1986;7(3):177-188. PubMedGoogle ScholarCrossref
Wright  IC, Rabe-Hesketh  S, Woodruff  PW, David  AS, Murray  RM, Bullmore  ET.  Meta-analysis of regional brain volumes in schizophrenia.  Am J Psychiatry. 2000;157(1):16-25. PubMedGoogle ScholarCrossref
Sutton  AJ.  Methods for Meta-analysis in Medical Research. New York, NY: John Wiley & Sons; 2000
Higgins  JP, Thompson  SG, Deeks  JJ, Altman  DG.  Measuring inconsistency in meta-analyses.  BMJ. 2003;327(7414):557-560. PubMedGoogle ScholarCrossref
Egger  M, Davey Smith  G, Schneider  M, Minder  C.  Bias in meta-analysis detected by a simple, graphical test.  BMJ. 1997;315(7109):629-634. PubMedGoogle ScholarCrossref
Spitzer  RL, Endicott  J, Robins  E.  Research diagnostic criteria: rationale and reliability.  Arch Gen Psychiatry. 1978;35(6):773-782.Google ScholarCrossref
Chen  YF.  Chinese Classification of Mental Disorders (CCMD-3): towards integration in international classification.  Psychopathology. 2002;35(2-3):171-175.Google ScholarCrossref
Scott  ML, Golden  CJ, Ruedrich  SL, Bishop  RJ.  Ventricular enlargement in major depression.  Psychiatry Res. 1983;8(2):91-93. PubMedGoogle ScholarCrossref
Iacono  WG, Smith  GN, Moreau  M, Beiser  M, Fleming  JA, Lin  TY, Flak  B.  Ventricular and sulcal size at the onset of psychosis.  Am J Psychiatry. 1988;145(7):820-824. PubMedGoogle ScholarCrossref
Pearlson  GD, Rabins  PV, Kim  WS, Speedie  LJ, Moberg  PJ, Burns  A, Bascom  MJ.  Structural brain CT changes and cognitive deficits in elderly depressives with and without reversible dementia (“pseudodementia”).  Psychol Med. 1989;19(3):573-584. PubMedGoogle ScholarCrossref
Andreasen  NC, Swayze  V  II, Flaum  M, Alliger  R, Cohen  G.  Ventricular abnormalities in affective disorder: clinical and demographic correlates.  Am J Psychiatry. 1990;147(7):893-900. PubMedGoogle ScholarCrossref
Coffey  CE, Figiel  GS, Djang  WT, Weiner  RD.  Subcortical hyperintensity on magnetic resonance imaging: a comparison of normal and depressed elderly subjects.  Am J Psychiatry. 1990;147(2):187-189. PubMedGoogle ScholarCrossref
Harvey  I, Williams  M, Toone  BK, Lewis  SW, Turner  SW, McGuffin  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 ScholarCrossref
Zubenko  GS, Sullivan  P, Nelson  JP, Belle  SH, Huff  FJ, Wolf  GL.  Brain imaging abnormalities in mental disorders of late life.  Arch Neurol. 1990;47(10):1107-1111. PubMedGoogle ScholarCrossref
Husain  MM, McDonald  WM, Doraiswamy  PM, Figiel  GS, Na  C, Escalona  PR, Boyko  OB, Nemeroff  CB, Krishnan  KR.  A magnetic resonance imaging study of putamen nuclei in major depression.  Psychiatry Res. 1991;40(2):95-99. PubMedGoogle ScholarCrossref
Lammers  CH, Doraiswamy  PM, Husain  MM, Figiel  GS, Lurie  SN, Boyko  OB, Ellinwood  EH  Jr, Nemeroff  CB, Krishnan  KR.  MRI of corpus callosum and septum pellucidum in depression.  Biol Psychiatry. 1991;29(3):300-301. PubMedGoogle ScholarCrossref
Lewine  RR, Risch  SC, Risby  E, Stipetic  M, Jewart  RD, Eccard  M, Caudle  J, Pollard  W.  Lateral ventricle-brain ratio and balance between CSF HVA and 5-HIAA in schizophrenia.  Am J Psychiatry. 1991;148(9):1189-1194. PubMedGoogle ScholarCrossref
Brown  FW, Lewine  RJ, Hudgins  PA, Risch  SC.  White matter hyperintensity signals in psychiatric and nonpsychiatric subjects.  Am J Psychiatry. 1992;149(5):620-625. PubMedGoogle ScholarCrossref
Guze  BH, Szuba  MP.  Leukoencephalopathy and major depression: a preliminary report.  Psychiatry Res. 1992;45(3):169-175. PubMedGoogle ScholarCrossref
Krishnan  KR, McDonald  WM, Escalona  PR, Doraiswamy  PM, Na  C, Husain  MM, Figiel  GS, Boyko  OB, Ellinwood  EH, Nemeroff  CB.  Magnetic resonance imaging of the caudate nuclei in depression: preliminary observations.  Arch Gen Psychiatry. 1992;49(7):553-557. PubMedGoogle ScholarCrossref
Lauer  CJ, Wiegand  M, Krieg  JC.  All-night electroencephalographic sleep and cranial computed tomography in depression: a study of unipolar and bipolar patients.  Eur Arch Psychiatry Clin Neurosci. 1992;242(2-3):59-68. PubMedGoogle ScholarCrossref
Shah  SA, Doraiswamy  PM, Husain  MM, Escalona  PR, Na  C, Figiel  GS, Patterson  LJ, Ellinwood  EH  Jr, McDonald  WM, Boyko  OB, Nemeroff  CB, Krishnan  KR.  Posterior fossa abnormalities in major depression: a controlled magnetic resonance imaging study.  Acta Psychiatr Scand. 1992;85(6):474-479. PubMedGoogle ScholarCrossref
Axelson  DA, Doraiswamy  PM, McDonald  WM, Boyko  OB, Tupler  LA, Patterson  LJ, Nemeroff  CB, Ellinwood  EH  Jr, Krishnan  KR.  Hypercortisolemia and hippocampal changes in depression.  Psychiatry Res. 1993;47(2):163-173. PubMedGoogle ScholarCrossref
Krishnan  KR, McDonald  WM, Doraiswamy  PM, Tupler  LA, Husain  M, Boyko  OB, Figiel  GS, Ellinwood  EH  Jr.  Neuroanatomical substrates of depression in the elderly.  Eur Arch Psychiatry Clin Neurosci. 1993;243(1):41-46. PubMedGoogle ScholarCrossref
Lisanby  SH, McDonald  WM, Massey  EW, Doraiswamy  PM, Rozear  M, Boyko  OB, Krishnan  KR, Nemeroff  C.  Diminished subcortical nuclei volumes in Parkinson's disease by MR imaging.  J Neural Transm Suppl. 1993;40:13-21. PubMedGoogle Scholar
Wu  JC, Buchsbaum  MS, Johnson  JC, Hershey  TG, Wagner  EA, Teng  C, Lottenberg  S.  Magnetic resonance and positron emission tomography imaging of the corpus callosum: size, shape and metabolic rate in unipolar depression.  J Affect Disord. 1993;28(1):15-25. PubMedGoogle ScholarCrossref
Lesser  IM, Mena  I, Boone  KB, Miller  BL, Mehringer  CM, Wohl  M.  Reduction of cerebral blood flow in older depressed patients.  Arch Gen Psychiatry. 1994;51(9):677-686. PubMedGoogle ScholarCrossref
Miller  DS, Kumar  A, Yousem  DM, Gottlieb  GL.  MRI high-intensity signals in late-life depression and Alzheimer's disease: a comparison of subjects without major vascular risk factors.  Am J Geriatr Psychiatry. 1994;2(4):332-337.Google ScholarCrossref
Dupont  RM, Jernigan  TL, Heindel  W, Butters  N, Shafer  K, Wilson  T, Hesselink  J, Gillin  JC.  Magnetic resonance imaging and mood disorders: localization of white matter and other subcortical abnormalities.  Arch Gen Psychiatry. 1995;52(9):747-755. PubMedGoogle ScholarCrossref
Dupont  RM, Butters  N, Schafer  K, Wilson  T, Hesselink  J, Gillin  JC.  Diagnostic specificity of focal white matter abnormalities in bipolar and unipolar mood disorder.  Biol Psychiatry. 1995;38(7):482-486. PubMedGoogle ScholarCrossref
Lewine  RR, Hudgins  P, Brown  F, Caudle  J, Risch  SC.  Differences in qualitative brain morphology findings in schizophrenia, major depression, bipolar disorder, and normal volunteers.  Schizophr Res. 1995;15(3):253-259. PubMedGoogle ScholarCrossref
Wurthmann  C, Bogerts  B, Falkai  P.  Brain morphology assessed by computed tomography in patients with geriatric depression, patients with degenerative dementia, and normal control subjects.  Psychiatry Res. 1995;61(2):103-111. PubMedGoogle ScholarCrossref
Elkis  H, Friedman  L, Buckley  PF, Lee  HS, Lys  C, Kaufman  B, Meltzer  HY.  Increased prefrontal sulcal prominence in relatively young patients with unipolar major depression.  Psychiatry Res. 1996;67(2):123-134. PubMedGoogle ScholarCrossref
Greenwald  BS, Kramer-Ginsberg  E, Krishnan  RR, Ashtari  M, Aupperle  PM, Patel  M.  MRI signal hyperintensities in geriatric depression.  Am J Psychiatry. 1996;153(9):1212-1215. PubMedGoogle ScholarCrossref
Keshavan  MS, Mulsant  BH, Sweet  RA, Pasternak  R, Zubenko  GS, Krishnan  RR.  MRI changes in schizophrenia in late life: a preliminary controlled study.  Psychiatry Res. 1996;60(2-3):117-123. PubMedGoogle ScholarCrossref
Lesser  IM, Boone  KB, Mehringer  CM, Wohl  MA, Miller  BL, Berman  NG.  Cognition and white matter hyperintensities in older depressed patients.  Am J Psychiatry. 1996;153(10):1280-1287. PubMedGoogle ScholarCrossref
Marchesi  C, Silvestrini  C, Ponari  O, Volpi  R, Chiodera  P, Coiro  V.  Unreliability of TRH test but not dexamethasone suppression test as a marker of depression in chronic vasculopathic patients.  Biol Psychiatry. 1996;40(7):637-641. PubMedGoogle ScholarCrossref
Drevets  WC, Price  JL, Simpson  JR  Jr, Todd  RD, Reich  T, Vannier  M, Raichle  ME.  Subgenual prefrontal cortex abnormalities in mood disorders.  Nature. 1997;386(6627):824-827. PubMedGoogle ScholarCrossref
Kumar  A, Miller  D, Ewbank  D, Yousem  D, Newberg  A, Samuels  S, Cowell  P, Gottlieb  G.  Quantitative anatomic measures and comorbid medical illness in late-life major depression.  Am J Geriatr Psychiatry. 1997;5(1):15-25. PubMedGoogle ScholarCrossref
Pantel  J, Schröder  J, Essig  M, Popp  D, Dech  H, Knopp  MV, Schad  LR, Eysenbach  K, Backenstrass  M, Friedlinger  M.  Quantitative magnetic resonance imaging in geriatric depression and primary degenerative dementia.  J Affect Disord. 1997;42(1):69-83. PubMedGoogle ScholarCrossref
Pillay  SS, Yurgelun-Todd  DA, Bonello  CM, Lafer  B, Fava  M, Renshaw  PF.  A quantitative magnetic resonance imaging study of cerebral and cerebellar gray matter volume in primary unipolar major depression: relationship to treatment response and clinical severity.  Biol Psychiatry. 1997;42(2):79-84. PubMedGoogle ScholarCrossref
Kumar  A, Jin  Z, Bilker  W, Udupa  J, Gottlieb  G.  Late-onset minor and major depression: early evidence for common neuroanatomical substrates detected by using MRI.  Proc Natl Acad Sci U S A. 1998;95(13):7654-7658. PubMedGoogle ScholarCrossref
Parashos  IA, Tupler  LA, Blitchington  T, Krishnan  KR.  Magnetic-resonance morphometry in patients with major depression.  Psychiatry Res. 1998;84(1):7-15. PubMedGoogle ScholarCrossref
Pillay  SS, Renshaw  PF, Bonello  CM, Lafer  BC, Fava  M, Yurgelun-Todd  D.  A quantitative magnetic resonance imaging study of caudate and lenticular nucleus gray matter volume in primary unipolar major depression: relationship to treatment response and clinical severity.  Psychiatry Res. 1998;84(2-3):61-74. PubMedGoogle ScholarCrossref
Sheline  YI, Gado  MH, Price  JL.  Amygdala core nuclei volumes are decreased in recurrent major depression.  Neuroreport. 1998;9(9):2023-2028. PubMedGoogle ScholarCrossref
Ashtari  M, Greenwald  BS, Kramer-Ginsberg  E, Hu  J, Wu  H, Patel  M, Aupperle  P, Pollack  S.  Hippocampal/amygdala volumes in geriatric depression.  Psychol Med. 1999;29(3):629-638. PubMedGoogle ScholarCrossref
Kramer-Ginsberg  E, Greenwald  BS, Krishnan  KR, Christiansen  B, Hu  J, Ashtari  M, Patel  M, Pollack  S.  Neuropsychological functioning and MRI signal hyperintensities in geriatric depression.  Am J Psychiatry. 1999;156(3):438-444. PubMedGoogle Scholar
Lenze  E, Cross  D, McKeel  D, Neuman  RJ, Sheline  YI.  White matter hyperintensities and gray matter lesions in physically healthy depressed subjects.  Am J Psychiatry. 1999;156(10):1602-1607. PubMedGoogle ScholarCrossref
Lenze  EJ, Sheline  YI.  Absence of striatal volume differences between depressed subjects with no comorbid medical illness and matched comparison subjects.  Am J Psychiatry. 1999;156(12):1989-1991. PubMedGoogle Scholar
Bremner  JD, Narayan  M, Anderson  ER, Staib  LH, Miller  HL, Charney  DS.  Hippocampal volume reduction in major depression.  Am J Psychiatry. 2000;157(1):115-118. PubMedGoogle ScholarCrossref
Kumar  A, Bilker  W, Jin  Z, Udupa  J.  Atrophy and high intensity lesions: complementary neurobiological mechanisms in late-life major depression.  Neuropsychopharmacology. 2000;22(3):264-274. PubMedGoogle ScholarCrossref
Vakili  K, Pillay  SS, Lafer  B, Fava  M, Renshaw  PF, Bonello-Cintron  CM, Yurgelun-Todd  DA.  Hippocampal volume in primary unipolar major depression: a magnetic resonance imaging study.  Biol Psychiatry. 2000;47(12):1087-1090. PubMedGoogle ScholarCrossref
Caetano  SC, Sassi  R, Brambilla  P, Harenski  K, Nicoletti  M, Mallinger  AG, Frank  E, Kupfer  DJ, Keshavan  MS, Soares  JC.  MRI study of thalamic volumes in bipolar and unipolar patients and healthy individuals.  Psychiatry Res. 2001;108(3):161-168. PubMedGoogle ScholarCrossref
Greenwald  BS, Kramer-Ginsberg  E, Krishnan  KR, Hu  J, Ashtari  M, Wu  H, Aupperle  P, Patel  M, Pollack  S.  A controlled study of MRI signal hyperintensities in older depressed patients with and without hypertension.  J Am Geriatr Soc. 2001;49(9):1218-1225. PubMedGoogle ScholarCrossref
McIntosh  AM, Forrester  A, Lawrie  SM, Byrne  M, Harper  A, Kestelman  JN, Best  JJ, Johnstone  EC, Owens  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 ScholarCrossref
Novaretti  TM, Marcolin  MA, Meira  S  Jr, Gelás  PL, Baudelin  CG, Bottino  CM.  Subcortical hyperintensities on magnetic resonance imaging: a comparison of normal and depressed elderly subjects [in Portuguese].  Arq Neuropsiquiatr. 2001;59(3-B):754-760. PubMedGoogle ScholarCrossref
Rusch  BD, Abercrombie  HC, Oakes  TR, Schaefer  SM, Davidson  RJ.  Hippocampal morphometry in depressed patients and control subjects: relations to anxiety symptoms.  Biol Psychiatry. 2001;50(12):960-964. PubMedGoogle ScholarCrossref
Sassi  RB, Nicoletti  M, Brambilla  P, Harenski  K, Mallinger  AG, Frank  E, Kupfer  DJ, Keshavan  MS, Soares  JC.  Decreased pituitary volume in patients with bipolar disorder.  Biol Psychiatry. 2001;50(4):271-280. PubMedGoogle ScholarCrossref
Botteron  KN, Raichle  ME, Drevets  WC, Heath  AC, Todd  RD.  Volumetric reduction in left subgenual prefrontal cortex in early onset depression.  Biol Psychiatry. 2002;51(4):342-344. PubMedGoogle ScholarCrossref
Brambilla  P, Nicoletti  MA, Harenski  K, Sassi  RB, Mallinger  AG, Frank  E, Kupfer  DJ, Keshavan  MS, Soares  JC.  Anatomical MRI study of subgenual prefrontal cortex in bipolar and unipolar subjects.  Neuropsychopharmacology. 2002;27(5):792-799. PubMedGoogle ScholarCrossref
Bremner  JD, Vythilingam  M, Vermetten  E, Nazeer  A, Adil  J, Khan  S, Staib  LH, Charney  DS.  Reduced volume of orbitofrontal cortex in major depression.  Biol Psychiatry. 2002;51(4):273-279. PubMedGoogle ScholarCrossref
Frodl  T, Meisenzahl  E, Zetzsche  T, Bottlender  R, Born  C, Groll  C, Jäger  M, Leinsinger  G, Hahn  K, Möller  HJ.  Enlargement of the amygdala in patients with a first episode of major depression.  Biol Psychiatry. 2002;51(9):708-714. PubMedGoogle ScholarCrossref
Nolan  CL, Moore  GJ, Madden  R, Farchione  T, Bartoi  M, Lorch  E, Stewart  CM, Rosenberg  DR.  Prefrontal cortical volume in childhood-onset major depression: preliminary findings.  Arch Gen Psychiatry. 2002;59(2):173-179. PubMedGoogle ScholarCrossref
Pujol  J, Cardoner  N, Benlloch  L, Urretavizcaya  M, Deus  J, Losilla  JM, Capdevila  A, Vallejo  J.  CSF spaces of the Sylvian fissure region in severe melancholic depression.  Neuroimage. 2002;15(1):103-106. PubMedGoogle ScholarCrossref
Salokangas  RK, Cannon  T, Van Erp  T, Ilonen  T, Taiminen  T, Karlsson  H, Lauerma  H, Leinonen  KM, Wallenius  E, Kaljonen  A, Syvälahti  E, Vilkman  H, Alanen  A, Hietala  J.  Structural magnetic resonance imaging in patients with first-episode schizophrenia, psychotic and severe non-psychotic depression and healthy controls: results of the Schizophrenia and Affective Psychoses (SAP) project.  Br J Psychiatry Suppl. 2002;43:s58-s65. PubMedGoogle ScholarCrossref
Steingard  RJ, Renshaw  PF, Hennen  J, Lenox  M, Cintron  CB, Young  AD, Connor  DF, Au  TH, Yurgelun-Todd  DA.  Smaller frontal lobe white matter volumes in depressed adolescents.  Biol Psychiatry. 2002;52(5):413-417. PubMedGoogle ScholarCrossref
Tupler  LA, Krishnan  KR, McDonald  WM, Dombeck  CB, D’Souza  S, Steffens  DC.  Anatomic location and laterality of MRI signal hyperintensities in late-life depression.  J Psychosom Res. 2002;53(2):665-676. PubMedGoogle ScholarCrossref
Agid  R, Levin  T, Gomori  JM, Lerer  B, Bonne  O.  T2-weighted image hyperintensities in major depression: focus on the basal ganglia.  Int J Neuropsychopharmacol. 2003;6(3):215-224. PubMedGoogle ScholarCrossref
Almeida  OP, Burton  EJ, Ferrier  N, McKeith  IG, O’Brien  JT.  Depression with late onset is associated with right frontal lobe atrophy.  Psychol Med. 2003;33(4):675-681. PubMedGoogle ScholarCrossref
Frodl  T, Meisenzahl  EM, Zetzsche  T, Born  C, Jäger  M, Groll  C, Bottlender  R, Leinsinger  G, Möller  HJ.  Larger amygdala volumes in first depressive episode as compared to recurrent major depression and healthy control subjects.  Biol Psychiatry. 2003;53(4):338-344. PubMedGoogle ScholarCrossref
Lacerda  AL, Nicoletti  MA, Brambilla  P, Sassi  RB, Mallinger  AG, Frank  E, Kupfer  DJ, Keshavan  MS, Soares  JC.  Anatomical MRI study of basal ganglia in major depressive disorder.  Psychiatry Res. 2003;124(3):129-140. PubMedGoogle ScholarCrossref
MacMillan  S, Szeszko  PR, Moore  GJ, Madden  R, Lorch  E, Ivey  J, Banerjee  SP, Rosenberg  DR.  Increased amygdala: hippocampal volume ratios associated with severity of anxiety in pediatric major depression.  J Child Adolesc Psychopharmacol. 2003;13(1):65-73. PubMedGoogle ScholarCrossref
MacQueen  GM, Campbell  S, McEwen  BS, Macdonald  K, Amano  S, Joffe  RT, Nahmias  C, Young  LT.  Course of illness, hippocampal function, and hippocampal volume in major depression.  Proc Natl Acad Sci U S A. 2003;100(3):1387-1392. PubMedGoogle ScholarCrossref
Posener  JA, Wang  L, Price  JL, Gado  MH, Province  MA, Miller  MI, Babb  CM, Csernansky  JG.  High-dimensional mapping of the hippocampus in depression.  Am J Psychiatry. 2003;160(1):83-89. PubMedGoogle ScholarCrossref
Sassi  RB, Brambilla  P, Nicoletti  M, Mallinger  AG, Frank  E, Kupfer  DJ, Keshavan  MS, Soares  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 ScholarCrossref
Sheline  YI, Gado  MH, Kraemer  HC.  Untreated depression and hippocampal volume loss.  Am J Psychiatry. 2003;160(8):1516-1518. PubMedGoogle ScholarCrossref
Silverstone  T, McPherson  H, Li  Q, Doyle  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 ScholarCrossref
Baldwin  R, Jeffries  S, Jackson  A, Sutcliffe  C, Thacker  N, Scott  M, Burns  A.  Treatment response in late-onset depression: relationship to neuropsychological, neuroradiological and vascular risk factors.  Psychol Med. 2004;34(1):125-136. PubMedGoogle ScholarCrossref
Ballmaier  M, Kumar  A, Thompson  PM, Narr  KL, Lavretsky  H, Estanol  L, Deluca  H, Toga  AW.  Localizing gray matter deficits in late-onset depression using computational cortical pattern matching methods.  Am J Psychiatry. 2004;161(11):2091-2099. PubMedGoogle ScholarCrossref
Ballmaier  M, Sowell  ER, Thompson  PM, Kumar  A, Narr  KL, Lavretsky  H, Welcome  SE, DeLuca  H, Toga  AW.  Mapping brain size and cortical gray matter changes in elderly depression.  Biol Psychiatry. 2004;55(4):382-389. PubMedGoogle ScholarCrossref
Caetano  SC, Hatch  JP, Brambilla  P, Sassi  RB, Nicoletti  M, Mallinger  AG, Frank  E, Kupfer  DJ, Keshavan  MS, Soares  JC.  Anatomical MRI study of hippocampus and amygdala in patients with current and remitted major depression.  Psychiatry Res. 2004;132(2):141-147. PubMedGoogle ScholarCrossref
Hastings  RS, Parsey  RV, Oquendo  MA, Arango  V, Mann  JJ.  Volumetric analysis of the prefrontal cortex, amygdala, and hippocampus in major depression.  Neuropsychopharmacology. 2004;29(5):952-959. PubMedGoogle ScholarCrossref
Janssen  J, Hulshoff Pol  HE, Lampe  IK, Schnack  HG, de Leeuw  FE, Kahn  RS, Heeren  TJ.  Hippocampal changes and white matter lesions in early-onset depression.  Biol Psychiatry. 2004;56(11):825-831. PubMedGoogle ScholarCrossref
Lacerda  AL, Keshavan  MS, Hardan  AY, Yorbik  O, Brambilla  P, Sassi  RB, Nicoletti  M, Mallinger  AG, Frank  E, Kupfer  DJ, Soares  JC.  Anatomic evaluation of the orbitofrontal cortex in major depressive disorder.  Biol Psychiatry. 2004;55(4):353-358. PubMedGoogle ScholarCrossref
Lange  C, Irle  E.  Enlarged amygdala volume and reduced hippocampal volume in young women with major depression.  Psychol Med. 2004;34(6):1059-1064. PubMedGoogle ScholarCrossref
Lavretsky  H, Kurbanyan  K, Ballmaier  M, Mintz  J, Toga  A, Kumar  A.  Sex differences in brain structure in geriatric depression.  Am J Geriatr Psychiatry. 2004;12(6):653-657. PubMedGoogle ScholarCrossref
Lloyd  AJ, Ferrier  IN, Barber  R, Gholkar  A, Young  AH, O’Brien  JT.  Hippocampal volume change in depression: late- and early-onset illness compared.  Br J Psychiatry. 2004;184:488-495. PubMedGoogle ScholarCrossref
MacMaster  FP, Kusumakar  V.  Hippocampal volume in early onset depression.  BMC Med. 2004;2:2PubMedGoogle ScholarCrossref
MacMaster  FP, Kusumakar  V.  MRI study of the pituitary gland in adolescent depression.  J Psychiatr Res. 2004;38(3):231-236. PubMedGoogle ScholarCrossref
Supprian  T, Reiche  W, Schmitz  B, Grunwald  I, Backens  M, Hofmann  E, Georg  T, Falkai  P, Reith  W.  MRI of the brainstem in patients with major depression, bipolar affective disorder and normal controls.  Psychiatry Res. 2004;131(3):269-276. PubMedGoogle ScholarCrossref
Vythilingam  M, Vermetten  E, Anderson  GM, Luckenbaugh  D, Anderson  ER, Snow  J, Staib  LH, Charney  DS, Bremner  JD.  Hippocampal volume, memory, and cortisol status in major depressive disorder: effects of treatment.  Biol Psychiatry. 2004;56(2):101-112. PubMedGoogle ScholarCrossref
Xia  J, Chen  J, Zhou  Y, Zhang  J, Yang  B, Xia  L, Wang  C.  Volumetric MRI analysis of the amygdala and hippocampus in subjects with major depression.  J Huazhong Univ Sci Technolog Med Sci. 2004;24(5):500-502, 506. PubMedGoogle ScholarCrossref
Chen  CS, Tsai  JC, Tsang  HY, Kuo  YT, Lin  HF, Chiang  IC, Devanand  DP.  Homocysteine levels, MTHFR C677T genotype, and MRI hyperintensities in late-onset major depressive disorder.  Am J Geriatr Psychiatry. 2005;13(10):869-875. PubMedGoogle ScholarCrossref
Coryell  W, Nopoulos  P, Drevets  W, Wilson  T, Andreasen  NC.  Subgenual prefrontal cortex volumes in major depressive disorder and schizophrenia: diagnostic specificity and prognostic implications.  Am J Psychiatry. 2005;162(9):1706-1712. PubMedGoogle ScholarCrossref
Hickie  I, Naismith  S, Ward  PB, Turner  K, Scott  E, Mitchell  P, Wilhelm  K, Parker  G.  Reduced hippocampal volumes and memory loss in patients with early- and late-onset depression.  Br J Psychiatry. 2005;186:197-202. PubMedGoogle ScholarCrossref
Iosifescu  DV, Papakostas  GI, Lyoo  IK, Lee  HK, Renshaw  PF, Alpert  JE, Nierenberg  A, Fava  M.  Brain MRI white matter hyperintensities and one-carbon cycle metabolism in non-geriatric outpatients with major depressive disorder (part I).  Psychiatry Res. 2005;140(3):291-299. PubMedGoogle ScholarCrossref
Lacerda  AL, Brambilla  P, Sassi  RB, Nicoletti  MA, Mallinger  AG, Frank  E, Kupfer  DJ, Keshavan  MS, Soares  JC.  Anatomical MRI study of corpus callosum in unipolar depression.  J Psychiatr Res. 2005;39(4):347-354. PubMedGoogle ScholarCrossref
Lavretsky  H, Roybal  DJ, Ballmaier  M, Toga  AW, Kumar  A.  Antidepressant exposure may protect against decrement in frontal gray matter volumes in geriatric depression.  J Clin Psychiatry. 2005;66(8):964-967. PubMedGoogle ScholarCrossref
Lin  HF, Kuo  YT, Chiang  IC, Chen  HM, Chen  CS.  Structural abnormality on brain magnetic resonance imaging in late-onset major depressive disorder.  Kaohsiung J Med Sci. 2005;21(9):405-411. PubMedGoogle ScholarCrossref
Rosso  IM, Cintron  CM, Steingard  RJ, Renshaw  PF, Young  AD, Yurgelun-Todd  DA.  Amygdala and hippocampus volumes in pediatric major depression.  Biol Psychiatry. 2005;57(1):21-26. PubMedGoogle ScholarCrossref
Taylor  WD, Steffens  DC, Payne  ME, MacFall  JR, Marchuk  DA, Svenson  IK, Krishnan  KR.  Influence of serotonin transporter promoter region polymorphisms on hippocampal volumes in late-life depression.  Arch Gen Psychiatry. 2005;62(5):537-544. PubMedGoogle ScholarCrossref
Caetano  SC, Kaur  S, Brambilla  P, Nicoletti  M, Hatch  JP, Sassi  RB, Mallinger  AG, Keshavan  MS, Kupfer  DJ, Frank  E, Soares  JC.  Smaller cingulate volumes in unipolar depressed patients.  Biol Psychiatry. 2006;59(8):702-706. PubMedGoogle ScholarCrossref
Frodl  T, Schaub  A, Banac  S, Charypar  M, Jäger  M, Kümmler  P, Bottlender  R, Zetzsche  T, Born  C, Leinsinger  G, Reiser  M, Möller  HJ, Meisenzahl  EM.  Reduced hippocampal volume correlates with executive dysfunctioning in major depression.  J Psychiatry Neurosci. 2006;31(5):316-323. PubMedGoogle Scholar
Hannestad  J, Taylor  WD, McQuoid  DR, Payne  ME, Krishnan  KR, Steffens  DC, Macfall  JR.  White matter lesion volumes and caudate volumes in late-life depression.  Int J Geriatr Psychiatry. 2006;21(12):1193-1198. PubMedGoogle ScholarCrossref
Iosifescu  DV, Renshaw  PF, Lyoo  IK, Lee  HK, Perlis  RH, Papakostas  GI, Nierenberg  AA, Fava  M.  Brain white-matter hyperintensities and treatment outcome in major depressive disorder.  Br J Psychiatry. 2006;188:180-185. PubMedGoogle ScholarCrossref
MacMaster  FP, Russell  A, Mirza  Y, Keshavan  MS, Taormina  SP, Bhandari  R, Boyd  C, Lynch  M, Rose  M, Ivey  J, Moore  GJ, Rosenberg  DR.  Pituitary volume in treatment-naïve pediatric major depressive disorder.  Biol Psychiatry. 2006;60(8):862-866. PubMedGoogle ScholarCrossref
Naish  JH, Baldwin  RC, Patankar  T, Jeffries  S, Burns  AS, Taylor  CJ, Waterton  JC, Jackson  A.  Abnormalities of CSF flow patterns in the cerebral aqueduct in treatment-resistant late-life depression: a potential biomarker of microvascular angiopathy.  Magn Reson Med. 2006;56(3):509-516. PubMedGoogle ScholarCrossref
Saylam  C, Uçerler  H, Kitiş  O, Ozand  E, Gönül  AS.  Reduced hippocampal volume in drug-free depressed patients.  Surg Radiol Anat. 2006;28(1):82-87. PubMedGoogle ScholarCrossref
Velakoulis  D, Wood  SJ, Wong  MT, McGorry  PD, Yung  A, Phillips  L, Smith  D, Brewer  W, Proffitt  T, Desmond  P, Pantelis  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 ScholarCrossref
Weniger  G, Lange  C, Irle  E.  Abnormal size of the amygdala predicts impaired emotional memory in major depressive disorder.  J Affect Disord. 2006;94(1-3):219-229. PubMedGoogle ScholarCrossref
Caetano  SC, Fonseca  M, Hatch  JP, Olvera  RL, Nicoletti  M, Hunter  K, Lafer  B, Pliszka  SR, Soares  JC.  Medial temporal lobe abnormalities in pediatric unipolar depression.  Neurosci Lett. 2007;427(3):142-147. PubMedGoogle ScholarCrossref
Colla  M, Kronenberg  G, Deuschle  M, Meichel  K, Hagen  T, Bohrer  M, Heuser  I.  Hippocampal volume reduction and HPA-system activity in major depression.  J Psychiatr Res. 2007;41(7):553-560. PubMedGoogle ScholarCrossref
Hickie  IB, Naismith  SL, Ward  PB, Scott  EM, Mitchell  PB, Schofield  PR, Scimone  A, Wilhelm  K, Parker  G.  Serotonin transporter gene status predicts caudate nucleus but not amygdala or hippocampal volumes in older persons with major depression.  J Affect Disord. 2007;98(1-2):137-142. PubMedGoogle ScholarCrossref
Lavretsky  H, Ballmaier  M, Pham  D, Toga  A, Kumar  A.  Neuroanatomical characteristics of geriatric apathy and depression: a magnetic resonance imaging study.  Am J Geriatr Psychiatry. 2007;15(5):386-394. PubMedGoogle ScholarCrossref
Maller  JJ, Daskalakis  ZJ, Fitzgerald  PB.  Hippocampal volumetrics in depression: the importance of the posterior tail.  Hippocampus. 2007;17(11):1023-1027. PubMedGoogle ScholarCrossref
Munn  MA, Alexopoulos  J, Nishino  T, Babb  CM, Flake  LA, Singer  T, Ratnanather  JT, Huang  H, Todd  RD, Miller  MI, Botteron  KN.  Amygdala volume analysis in female twins with major depression.  Biol Psychiatry. 2007;62(5):415-422. PubMedGoogle ScholarCrossref
Taylor  WD, Macfall  JR, Payne  ME, McQuoid  DR, Steffens  DC, Provenzale  JM, Krishnan  KR.  Orbitofrontal cortex volume in late life depression: influence of hyperintense lesions and genetic polymorphisms.  Psychol Med. 2007;37(12):1763-1773. PubMedGoogle ScholarCrossref
Andreescu  C, Butters  MA, Begley  A, Rajji  T, Wu  M, Meltzer  CC, Reynolds  CF  III, Aizenstein  H.  Gray matter changes in late life depression—a structural MRI analysis.  Neuropsychopharmacology. 2008;33(11):2566-2572. PubMedGoogle ScholarCrossref
Ballmaier  M, Narr  KL, Toga  AW, Elderkin-Thompson  V, Thompson  PM, Hamilton  L, Haroon  E, Pham  D, Heinz  A, Kumar  A.  Hippocampal morphology and distinguishing late-onset from early-onset elderly depression.  Am J Psychiatry. 2008;165(2):229-237. PubMedGoogle ScholarCrossref
Ballmaier  M, Kumar  A, Elderkin-Thompson  V, Narr  KL, Luders  E, Thompson  PM, Hojatkashani  C, Pham  D, Heinz  A, Toga  AW.  Mapping callosal morphology in early- and late-onset elderly depression: an index of distinct changes in cortical connectivity.  Neuropsychopharmacology. 2008;33(7):1528-1536. PubMedGoogle ScholarCrossref
Chen  HH, Rosenberg  DR, MacMaster  FP, Easter  PC, Caetano  SC, Nicoletti  M, Hatch  JP, Nery  FG, Soares  JC.  Orbitofrontal cortex volumes in medication naïve children with major depressive disorder: a magnetic resonance imaging study.  J Child Adolesc Psychopharmacol. 2008;18(6):551-556. PubMedGoogle ScholarCrossref
Eker  C, Ovali  GY, Ozan  E, Eker  OD, Kitis  O, Coburn  K, Gonul  AS.  No pituitary gland volume change in medication-free depressed patients.  Prog Neuropsychopharmacol Biol Psychiatry. 2008;32(7):1628-1632. PubMedGoogle ScholarCrossref
Elderkin-Thompson  V, Ballmaier  M, Hellemann  G, Pham  D, Lavretsky  H, Kumar  A.  Daily functioning and prefrontal brain morphology in healthy and depressed community-dwelling elderly.  Am J Geriatr Psychiatry. 2008;16(8):633-642. PubMedGoogle ScholarCrossref
Elderkin-Thompson  V, Hellemann  G, Pham  D, Kumar  A.  Prefrontal brain morphology and executive function in healthy and depressed elderly.  Int J Geriatr Psychiatry. 2009;24(5):459-468. PubMedGoogle ScholarCrossref
Frodl  T, Jäger  M, Born  C, Ritter  S, Kraft  E, Zetzsche  T, Bottlender  R, Leinsinger  G, Reiser  M, Möller  HJ, Meisenzahl  E.  Anterior cingulate cortex does not differ between patients with major depression and healthy controls, but relatively large anterior cingulate cortex predicts a good clinical course.  Psychiatry Res. 2008;163(1):76-83. PubMedGoogle ScholarCrossref
Keller  J, Shen  L, Gomez  RG, Garrett  A, Solvason  HB, Reiss  A, Schatzberg  AF.  Hippocampal and amygdalar volumes in psychotic and nonpsychotic unipolar depression.  Am J Psychiatry. 2008;165(7):872-880. PubMedGoogle ScholarCrossref
Lenze  SN, Xiong  C, Sheline  YI.  Childhood adversity predicts earlier onset of major depression but not reduced hippocampal volume.  Psychiatry Res. 2008;162(1):39-49. PubMedGoogle ScholarCrossref
MacMaster  FP, Mirza  Y, Szeszko  PR, Kmiecik  LE, Easter  PC, Taormina  SP, Lynch  M, Rose  M, Moore  GJ, Rosenberg  DR.  Amygdala and hippocampal volumes in familial early onset major depressive disorder.  Biol Psychiatry. 2008;63(4):385-390. PubMedGoogle ScholarCrossref
Matsuo  K, Rosenberg  DR, Easter  PC, MacMaster  FP, Chen  HH, Nicoletti  M, Caetano  SC, Hatch  JP, Soares  JC.  Striatal volume abnormalities in treatment-naïve patients diagnosed with pediatric major depressive disorder.  J Child Adolesc Psychopharmacol. 2008;18(2):121-131. PubMedGoogle ScholarCrossref
Tae  WS, Kim  SS, Lee  KU, Nam  EC, Kim  KW.  Validation of hippocampal volumes measured using a manual method and two automated methods (FreeSurfer and IBASPM) in chronic major depressive disorder.  Neuroradiology. 2008;50(7):569-581. PubMedGoogle ScholarCrossref
Zanetti  MV, Schaufelberger  MS, de Castro  CC, Menezes  PR, Scazufca  M, McGuire  PK, Murray  RM, Busatto  GF.  White-matter hyperintensities in first-episode psychosis.  Br J Psychiatry. 2008;193(1):25-30. PubMedGoogle ScholarCrossref
Bergouignan  L, Chupin  M, Czechowska  Y, Kinkingnéhun  S, Lemogne  C, Le Bastard  G, Lepage  M, Garnero  L, Colliot  O, Fossati  P.  Can voxel based morphometry, manual segmentation and automated segmentation equally detect hippocampal volume differences in acute depression?  Neuroimage. 2009;45(1):29-37. PubMedGoogle ScholarCrossref
Exner  C, Lange  C, Irle  E.  Impaired implicit learning and reduced pre-supplementary motor cortex size in early-onset major depression with melancholic features.  J Affect Disord. 2009;119(1-3):156-162. PubMedGoogle ScholarCrossref
Jessen  F, Schuhmacher  A, von Widdern  O, Guttenthaler  V, Hofels  S, Suliman  H, Scheef  L, Block  W, Urbach  H, Maier  W, Zobel  A.  No association of the Val66Met polymorphism of the brain-derived neurotrophic factor with hippocampal volume in major depression.  Psychiatr Genet. 2009;19(2):99-101. PubMedGoogle ScholarCrossref
Kronenberg  G, Tebartz van Elst  L, Regen  F, Deuschle  M, Heuser  I, Colla  M.  Reduced amygdala volume in newly admitted psychiatric in-patients with unipolar major depression.  J Psychiatr Res. 2009;43(13):1112-1117. PubMedGoogle ScholarCrossref
Kronmüller  KT, Schröder  J, Köhler  S, Götz  B, Victor  D, Unger  J, Giesel  F, Magnotta  V, Mundt  C, Essig  M, Pantel  J.  Hippocampal volume in first episode and recurrent depression.  Psychiatry Res. 2009;174(1):62-66. PubMedGoogle ScholarCrossref
Lorenzetti  V, Allen  NB, Fornito  A, Pantelis  C, De Plato  G, Ang  A, Yücel  M.  Pituitary gland volume in currently depressed and remitted depressed patients.  Psychiatry Res. 2009;172(1):55-60. PubMedGoogle ScholarCrossref
Milne  A, MacQueen  GM, Yucel  K, Soreni  N, Hall  GB.  Hippocampal metabolic abnormalities at first onset and with recurrent episodes of a major depressive disorder: a proton magnetic resonance spectroscopy study.  Neuroimage. 2009;47(1):36-41. PubMedGoogle ScholarCrossref
Pan  CC, McQuoid  DR, Taylor  WD, Payne  ME, Ashley-Koch  A, Steffens  DC.  Association analysis of the COMT/MTHFR genes and geriatric depression: an MRI study of the putamen.  Int J Geriatr Psychiatry. 2009;24(8):847-855. PubMedGoogle ScholarCrossref
Penttilä  J, Paillère-Martinot  ML, Martinot  JL, Ringuenet  D, Wessa  M, Houenou  J, Gallarda  T, Bellivier  F, Galinowski  A, Bruguière  P, Pinabel  F, Leboyer  M, Olié  JP, Duchesnay  E, Artiges  E, Mangin  JF, Cachia  A.  Cortical folding in patients with bipolar disorder or unipolar depression.  J Psychiatry Neurosci. 2009;34(2):127-135. PubMedGoogle Scholar
Pizzagalli  DA, Holmes  AJ, Dillon  DG, Goetz  EL, Birk  JL, Bogdan  R, Dougherty  DD, Iosifescu  DV, Rauch  SL, Fava  M.  Reduced caudate and nucleus accumbens response to rewards in unmedicated individuals with major depressive disorder.  Am J Psychiatry. 2009;166(6):702-710. PubMedGoogle ScholarCrossref
Sun  J, Maller  JJ, Daskalakis  ZJ, Furtado  CC, Fitzgerald  PB.  Morphology of the corpus callosum in treatment-resistant schizophrenia and major depression.  Acta Psychiatr Scand. 2009;120(4):265-273. PubMedGoogle ScholarCrossref
Tamburo  RJ, Siegle  GJ, Stetten  GD, Cois  CA, Butters  MA, Reynolds  CF  III, Aizenstein  HJ.  Amygdalae morphometry in late-life depression.  Int J Geriatr Psychiatry. 2009;24(8):837-846. PubMedGoogle ScholarCrossref
van Eijndhoven  P, van Wingen  G, van Oijen  K, Rijpkema  M, Goraj  B, Jan Verkes  R, Oude Voshaar  R, Fernández  G, Buitelaar  J, Tendolkar  I.  Amygdala volume marks the acute state in the early course of depression.  Biol Psychiatry. 2009;65(9):812-818. PubMedGoogle ScholarCrossref
Walterfang  M, Yücel  M, Barton  S, Reutens  DC, Wood  AG, Chen  J, Lorenzetti  V, Velakoulis  D, Pantelis  C, Allen  NB.  Corpus callosum size and shape in individuals with current and past depression.  J Affect Disord. 2009;115(3):411-420. PubMedGoogle ScholarCrossref
Kaymak  SU, Demir  B, Senturk  S, Tatar  I, Aldur  MM, Ulug  B.  Hippocampus, glucocorticoids and neurocognitive functions in patients with first-episode major depressive disorders.  Eur Arch Psychiatry Clin Neurosci. 2010;260(3):217-223. PubMedGoogle ScholarCrossref
Köhler  S, Thomas  AJ, Lloyd  A, Barber  R, Almeida  OP, O'Brien  JT.  White matter hyperintensities, cortisol levels, brain atrophy and continuing cognitive deficits in late-life depression.  Br J Psychiatry. 2010;196(2):143-149.Google ScholarCrossref
Lorenzetti  V, Allen  NB, Whittle  S, Yücel  M.  Amygdala volumes in a sample of current depressed and remitted depressed patients and healthy controls.  J Affect Disord. 2010;120(1-3):112-119. PubMedGoogle ScholarCrossref
Meisenzahl  EM, Seifert  D, Bottlender  R, Teipel  S, Zetzsche  T, Jäger  M, Koutsouleris  N, Schmitt  G, Scheuerecker  J, Burgermeister  B, Hampel  H, Rupprecht  T, Born  C, Reiser  M, Möller  HJ, Frodl  T.  Differences in hippocampal volume between major depression and schizophrenia: a comparative neuroimaging study.  Eur Arch Psychiatry Clin Neurosci. 2010;260(2):127-137. PubMedGoogle ScholarCrossref
Weber  K, Giannakopoulos  P, Delaloye  C, de Bilbao  F, Moy  G, Moussa  A, Rubio  MM, Ebbing  K, Meuli  R, Lazeyras  F, Meiler-Mititelu  C, Herrmann  FR, Gold  G, Canuto  A.  Volumetric MRI changes, cognition and personality traits in old age depression.  J Affect Disord. 2010;124(3):273-282. PubMedGoogle ScholarCrossref
Fazekas  F, Chawluk  JB, Alavi  A, Hurtig  HI, Zimmerman  RA.  MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging.  AJR Am J Roentgenol. 1987;149(2):351-356. PubMedGoogle ScholarCrossref
Scheltens  P, Barkhof  F, Leys  D, Pruvo  JP, Nauta  JJ, Vermersch  P, Steinling  M, Valk  J.  A semiquantative rating scale for the assessment of signal hyperintensities on magnetic resonance imaging.  J Neurol Sci. 1993;114(1):7-12. PubMedGoogle ScholarCrossref
Campbell  S, Marriott  M, Nahmias  C, MacQueen  GM.  Lower hippocampal volume in patients suffering from depression: a meta-analysis.  Am J Psychiatry. 2004;161(4):598-607. PubMedGoogle ScholarCrossref
Sapolsky  RM, Uno  H, Rebert  CS, Finch  CE.  Hippocampal damage associated with prolonged glucocorticoid exposure in primates.  J Neurosci. 1990;10(9):2897-2902. PubMedGoogle ScholarCrossref
Rubin  RT, Phillips  JJ, McCracken  JT, Sadow  TF.  Adrenal gland volume in major depression: relationship to basal and stimulated pituitary-adrenal cortical axis function.  Biol Psychiatry. 1996;40(2):89-97. PubMedGoogle ScholarCrossref
Amsterdam  JD, Marinelli  DL, Arger  P, Winokur  A.  Assessment of adrenal gland volume by computed tomography in depressed patients and healthy volunteers: a pilot study.  Psychiatry Res. 1987;21(3):189-197. PubMedGoogle ScholarCrossref
Nemeroff  CB, Krishnan  KR, Reed  D, Leder  R, Beam  C, Dunnick  NR.  Adrenal gland enlargement in major depression: a computed tomographic study.  Arch Gen Psychiatry. 1992;49(5):384-387. PubMedGoogle ScholarCrossref
Jacobs  BL, van Praag  H, Gage  FH.  Adult brain neurogenesis and psychiatry: a novel theory of depression.  Mol Psychiatry. 2000;5(3):262-269. PubMedGoogle ScholarCrossref
Malberg  JE, Eisch  AJ, Nestler  EJ, Duman  RS.  Chronic antidepressant treatment increases neurogenesis in adult rat hippocampus.  J Neurosci. 2000;20(24):9104-9110. PubMedGoogle ScholarCrossref
Boldrini  M, Underwood  MD, Hen  R, Rosoklija  GB, Dwork  AJ, John Mann  J, Arango  V.  Antidepressants increase neural progenitor cells in the human hippocampus.  Neuropsychopharmacology. 2009;34(11):2376-2389. PubMedGoogle ScholarCrossref
Pizzagalli  DA, Oakes  TR, Fox  AS, Chung  MK, Larson  CL, Abercrombie  HC, Schaefer  SM, Benca  RM, Davidson  RJ.  Functional but not structural subgenual prefrontal cortex abnormalities in melancholia.  Mol Psychiatry. 2004;9(4):325, 393-405. PubMedGoogle ScholarCrossref
Videbech  P.  PET measurements of brain glucose metabolism and blood flow in major depressive disorder: a critical review.  Acta Psychiatr Scand. 2000;101(1):11-20. PubMedGoogle ScholarCrossref
Rajkowska  G, Miguel-Hidalgo  JJ, Wei  J, Dilley  G, Pittman  SD, Meltzer  HY, Overholser  JC, Roth  BL, Stockmeier  CA.  Morphometric evidence for neuronal and glial prefrontal cell pathology in major depression.  Biol Psychiatry. 1999;45(9):1085-1098. PubMedGoogle ScholarCrossref
Knutson  B, Adams  CM, Fong  GW, Hommer  D.  Anticipation of increasing monetary reward selectively recruits nucleus accumbens.  J Neurosci. 2001;21(16):RC159 Accessed January 11, 2010 PubMedGoogle Scholar
Alexopoulos  GS, Meyers  BS, Young  RC, Campbell  S, Silbersweig  D, Charlson  M.  “Vascular depression” hypothesis.  Arch Gen Psychiatry. 1997;54(10):915-922. PubMedGoogle ScholarCrossref
Herrmann  LL, Le Masurier  M, Ebmeier  KP.  White matter hyperintensities in late life depression: a systematic review.  J Neurol Neurosurg Psychiatry. 2008;79(6):619-624. PubMedGoogle ScholarCrossref
Coffey  CE, Wilkinson  WE, Weiner  RD, Parashos  IA, Djang  WT, Webb  MC, Figiel  GS, Spritzer  CE.  Quantitative cerebral anatomy in depression: a controlled magnetic resonance imaging study.  Arch Gen Psychiatry. 1993;50(1):7-16. PubMedGoogle ScholarCrossref
Iidaka  T, Nakajima  T, Kawamoto  K, Fukuda  H, Suzuki  Y, Maehara  T, Shiraishi  H.  Signal hyperintensities on brain magnetic resonance imaging in elderly depressed patients.  Eur Neurol. 1996;36(5):293-299. PubMedGoogle ScholarCrossref
O’Brien  JT, Ames  D.  White matter lesions in depression and Alzheimer's disease [letter].  Br J Psychiatry. 1996;169(5):671PubMedGoogle ScholarCrossref
Shah  PJ, Ebmeier  KP, Glabus  MF, Goodwin  GM.  Cortical grey matter reductions associated with treatment-resistant chronic unipolar depression: controlled magnetic resonance imaging study.  Br J Psychiatry. 1998;172:527-532. PubMedGoogle ScholarCrossref
Bell-McGinty  S, Butters  MA, Meltzer  CC, Greer  PJ, Reynolds  CF  III, Becker  JT.  Brain morphometric abnormalities in geriatric depression: long-term neurobiological effects of illness duration.  Am J Psychiatry. 2002;159(8):1424-1427. PubMedGoogle ScholarCrossref
Vasic  N, Walter  H, Höse  A, Wolf  RC.  Gray matter reduction associated with psychopathology and cognitive dysfunction in unipolar depression: a voxel-based morphometry study.  J Affect Disord. 2008;109(1-2):107-116. PubMedGoogle ScholarCrossref
Wagner  G, Koch  K, Schachtzabel  C, Reichenbach  JR, Sauer  H, Schlösser  RG.  Enhanced rostral anterior cingulate cortex activation during cognitive control is related to orbitofrontal volume reduction in unipolar depression.  J Psychiatry Neurosci. 2008;33(3):199-208. PubMedGoogle Scholar
Frodl  T, Koutsouleris  N, Bottlender  R, Born  C, Jäger  M, Mörgenthaler  M, Scheuerecker  J, Zill  P, Baghai  T, Schüle  C, Rupprecht  R, Bondy  B, Reiser  M, Möller  HJ, Meisenzahl  EM.  Reduced gray matter brain volumes are associated with variants of the serotonin transporter gene in major depression.  Mol Psychiatry. 2008;13(12):1093-1101. PubMedGoogle ScholarCrossref
Egger  K, Schocke  M, Weiss  E, Auffinger  S, Esterhammer  R, Goebel  G, Walch  T, Mechtcheriakov  S, Marksteiner  J.  Pattern of brain atrophy in elderly patients with depression revealed by voxel-based morphometry.  Psychiatry Res. 2008;164(3):237-244. PubMedGoogle ScholarCrossref
Zou  K, Deng  W, Li  T, Zhang  B, Jiang  L, Huang  C, Sun  X, Sun  X.  Changes of brain morphometry in first-episode, drug-naïve, non-late-life adult patients with major depression: an optimized voxel-based morphometry study.  Biol Psychiatry. 2010;67(2):186-188. PubMedGoogle ScholarCrossref
Yuan  Y, Zhu  W, Zhang  Z, Bai  F, Yu  H, Shi  Y, Qian  Y, Liu  W, Jiang  T, You  J, Liu  Z.  Regional gray matter changes are associated with cognitive deficits in remitted geriatric depression: an optimized voxel-based morphometry study.  Biol Psychiatry. 2008;64(6):541-544. PubMedGoogle ScholarCrossref
Leung  KK, Lee  TM, Wong  MM, Li  LS, Yip  PS, Khong  PL.  Neural correlates of attention biases of people with major depressive disorder: a voxel-based morphometric study.  Psychol Med. 2009;39(7):1097-1106. PubMedGoogle ScholarCrossref
Tang  Y, Wang  F, Xie  G, Liu  J, Li  L, Su  L, Liu  Y, Hu  X, He  Z, Blumberg  HP.  Reduced ventral anterior cingulate and amygdala volumes in medication-naïve females with major depressive disorder: a voxel-based morphometric magnetic resonance imaging study.  Psychiatry Res. 2007;156(1):83-86. PubMedGoogle ScholarCrossref
Mak  AK, Wong  MM, Han  SH, Lee  TM.  Gray matter reduction associated with emotion regulation in female outpatients with major depressive disorder: a voxel-based morphometry study.  Prog Neuropsychopharmacol Biol Psychiatry. 2009;33(7):1184-1190. PubMedGoogle ScholarCrossref
Mahon  K, Burdick  KE, Szeszko  PR.  A role for white matter abnormalities in the pathophysiology of bipolar disorder.  Neurosci Biobehav Rev. 2010;34(4):533-554. PubMedGoogle ScholarCrossref
Craddock  N, Jones  I.  Genetics of bipolar disorder.  J Med Genet. 1999;36(8):585-594. PubMedGoogle ScholarCrossref
Sullivan  PF, Neale  MC, Kendler  KS.  Genetic epidemiology of major depression: review and meta-analysis.  Am J Psychiatry. 2000;157(10):1552-1562. PubMedGoogle ScholarCrossref
Moore  GJ, Bebchuk  JM, Wilds  IB, Chen  G, Manji  HK.  Lithium-induced increase in human brain grey matter.  Lancet. 2000;356(9237):1241-1242. PubMedGoogle ScholarCrossref
Bearden  CE, Thompson  PM, Dalwani  M, Hayashi  KM, Lee  AD, Nicoletti  M, Trakhtenbroit  M, Glahn  DC, Brambilla  P, Sassi  RB, Mallinger  AG, Frank  E, Kupfer  DJ, Soares  JC.  Greater cortical gray matter density in lithium-treated patients with bipolar disorder.  Biol Psychiatry. 2007;62(1):7-16. PubMedGoogle ScholarCrossref
Angst  J, Sellaro  R, Stassen  HH, Gamma  A.  Diagnostic conversion from depression to bipolar disorders: results of a long-term prospective study of hospital admissions.  J Affect Disord. 2005;84(2-3):149-157. PubMedGoogle ScholarCrossref