Logo of capMary Ann Liebert, Inc.Mary Ann Liebert, Inc.JournalsSearchAlerts
Journal of Child and Adolescent Psychopharmacology
J Child Adolesc Psychopharmacol. 2012 Jun; 22(3): 190–197.
PMCID: PMC3373217

Gray Matter Differences Between Healthy and Depressed Adolescents: A Voxel-Based Morphometry Study

Mujeeb U. Shad, M.D., MSCS,corresponding author1 Srirangam Muddasani, M.D.,2 and Uma Rao, M.D.3,4



Major depressive disorder (MDD) frequently begins during adolescence and is associated with significant morbidity and mortality. However, little is known about the neurobiology of adolescent depression. A better understanding of the neurobiology will be helpful in developing more effective preventive and treatment interventions for this highly disabling illness.


Using a voxel-based morphometric method, the study compared gray matter and white matter volumes in 22 adolescents with MDD and 22 age- and gender-matched normal controls.


Compared with controls, depressed adolescents had smaller gray matter volume in the frontal lobe and caudate nucleus bilaterally and right superior and middle temporal gyri. However, the groups did not differ significantly on white matter volume.


These findings in depressed adolescents are consistent with the previous findings of gray matter abnormalities in frontolimbic areas and the striatum in depressed adults and suggest the presence of these structural changes at the onset of depressive illness.


Adolescence is a highly vulnerable period marked by developmental brain changes during which many psychiatric disorders become clinically visible. Thus, research during adolescence may provide a unique opportunity to investigate the developmental neurobiology of underlying psychiatric disorders. One of the most disabling and common psychiatric disorders diagnosed during adolescence is major depressive disorder (MDD; Kessler et al. 2001), with a point prevalence of 3% to 9% and a cumulative prevalence of 20% by the end of adolescent years (Whitaker et al. 1990; Garrison et al. 1992; Lewinsohn et al. 1993; Shaffer et al. 1996). The clinical significance of adolescent depression is underscored by its close association with significantly impaired social, academic, and family functioning (Birmaher et al. 1996a; Birmaher et al. 1996b; Fergusson and Woodward 2002; Rao and Chen 2009). Additionally, MDD in adolescents carries a higher risk for substance abuse and other psychiatric co-morbidities (Whitaker et al. 1990a; Garrison et al. 1992; Lewinsohn et al. 1993; Shaffer et al. 1996; Rao et al. 2009), and it has been linked with increased hospitalizations, recurrent depression, antisocial behaviors, and suicide (National Adolescent Health Information Center 2006; Rao et al. 1993; Shaffer et al. 1996b; Weissman et al. 1999; Aalto-Setala et al. 2002; Rushton et al. 2002).

Despite the clinical significance of adolescent depression, there is limited understanding of its neurobiology. However, an increasing number of neuroimaging studies are reporting neuroanatomical deficits in specific brain regions in adolescents with MDD. Some morphometric studies reported white matter deficits in the frontolimbic (Steingard et al. 2002) and temporal (Li et al. 2007) regions. Region of interest (ROI) analyses displayed gray matter deficits in the hippocampus (MacMaster and Kusumaker 2004; Caetano et al. 2007; Rao et al. 2010) and prefrontal cortex (PFC; Nolan et al. 2002) in depressed youngsters. Similarly, gray matter deficits in the hippocampus (Bremner et al. 2000; Ballmaier et al. 2004; Campbell et al. 2004; Vasic et al. 2008), orbitofrontal cortex (OFC; Lacerda et al. 2003; Vasic et al. 2008), dorsolateral prefrontal cortex (DLPFC; Vasic et al. 2008), and cingulate gyrus (Vasic et al. 2008; Zhou et al. 2010) have been observed in adult subjects with MDD.

Although hand-drawn ROI-based morphometry remains the standard approach for neuroanatomical localization, it is labor intensive, and subject to error from poor interrater reliability or drift from template standards (Soloff et al. 2008). As a result, studies using ROI-based approach are generally hypothesis-driven studies, which typically focus on a few predefined ROIs without addressing multiple regions (networks) that might be implicated in MDD. In contrast to this, voxel-based morphometry (VBM; Ashburner and Friston 2001) is a computer-based, automated, morphometric technique for voxel-wise comparisons between groups of subjects, which is relatively free of rater bias, interrater variability, and drift from template standards (Soloff et al. 2008). Additionally, VBM does not require a priori hypotheses or definitions of anatomical areas and is highly efficient for relatively large sample studies. However, normalization of images to a standard template in VBM may result in some deformation of the original brain structure and a possible error in detecting small volume differences (Soloff et al. 2008).

To the best of our knowledge, there are no reported studies that examined gray matter differences in adolescents with depression using VBM methodology. In the current study, gray and white matter volumes were compared between adolescents diagnosed with MDD and normal controls using the VBM method to analyze magnetic resonance imaging (MRI) data (Good et al. 2001). Based on the findings from prior structural MRI studies, we hypothesized that adolescents with MDD will have smaller gray matter volume in the frontolimbic areas and striatum.



With approval from the Institutional Review Board, 22 depressed adolescents and 22 controls were recruited through local schools, mental health programs, and newspaper advertisements. The depressed adolescents met Diagnostic and Statistical Manual of Mental Disorders, 4th edition, Text revision (DSM-IV-TR) criteria (American Psychiatric Association 2000) for MDD with a minimum duration of 4 weeks. Adolescents with a current or prior history of mania, hypomania, substance use disorder symptoms, schizophrenia, schizoaffective disorder, or autism were excluded from the study. Subjects also were excluded if there was a family history of bipolar disorder. Controls were free from any type of psychopathology in their lifetime and were not included if any first-degree relative had history of a major psychiatric disorder. Prior to performing the research procedures, all adolescents under 18 years signed a written assent form and parents (or subjects ≥18 years) signed an informed consent document. All participants were medically healthy and free from alcohol or illicit drug use, as determined by physical examination, full chemistry panel, thyroid function tests, electrocardiogram, and urine drug screens.

Of 22 depressed subjects, 18 were psychotropic naïve. The remaining four participants were free from any psychotropic agent for at least 8 weeks before the study. These four participants took short-acting selective serotonin reuptake inhibitors prior to the study, and one of them also used a stimulant in childhood.

Diagnostic evaluation

The diagnosis of MDD and other psychiatric disorders was based on a semistructured instrument, the Schedule for Affective Disorders and Schizophrenia for School-Age Children—Present and Lifetime Version (K-SADS-PL; Kaufman et al. 1997), designed to ascertain present and lifetime history of psychiatric illness according to the DSM-IV-TR criteria (American Psychiatric Association 2000). The K-SADS-PL was administered separately to the adolescent and parent, and both were reinterviewed to resolve discrepancies. Summary scores were tabulated from information obtained from both informants.

Other assessments

The Children's Depression Rating Scale-Revised Version (CDRS-R; Poznanski et al. 1985) and Hamilton Rating Scale for Depression (HRSD; Hamilton 1960) were administered to assess the severity of depressive symptoms. The CDRS-R has established psychometric properties. Although HRSD is not normed for younger children, we administered this instrument in order to compare adolescent and adult samples in our developmental studies. The Family History-Research Diagnostic Criteria (FH-RDC) was used for the evaluation of psychiatric disorders in the first-degree family members (Andreasen et al. 1977). The FH-RDC is sensitive for obtaining information from knowledgeable relatives (Thompson et al. 1982). Socioeconomic status (SES) was assessed with the Hollingshead Four Factor Index of Social Status (Hollingshead 1975). Intelligence Quotient (IQ) was estimated from vocabulary and block design scores using the Wechsler Intelligence Scale for Children (WISC IV; Wechsler 2003) for ages <16 years and Wechsler Adult Intelligence Scale (WAIS III; Wechsler 1997) was employed for ages ≥16 years. The Tanner's scale was used to assess pubertal development (Marshall 1969, 1970).

The diagnostic and other assessments were performed by clinicians with a Bachelor's or Master's Degree in Psychology and a minimum of 3 months' training on the instruments. They achieved interrater reliability ≥0.85 with an expert rater (U.R.) on all symptom ratings and κ≥0.95 on diagnoses prior to conducting the semistructured interviews under the supervision of a trained interviewer. Independent interviews were conducted only after they were certified by the trained interviewer. All interviews were audio-taped, and a random set of assessments was independently coded by the expert rater (κ=1.00).

Magnetic resonance imaging

Structural images of the brain were obtained using a 1.5 Tesla scanner (General Electric Medical Systems, Milwaukee, Wisconsin) with a transmit/receive quadrature RF head coil. To avoid large motions, the head was immobilized with tightly-fitting foam padding and a strap was fastened across the forehead. High-resolution T1 images of the entire brain were acquired in the sagittal plane (3D SPGR pulse sequence: TR=19 ms; TE=4.6 ms; flip angle=30°; matrix=192×256; FOV=230 mm; slice thickness=1.2 mm).

VBM preprocessing

The data were analyzed by VBM methodology using Statistical Parametric Mapping, Version 5 (SPM5; Wellcome Department of Cognitive Neurology; www.fil.ion.ucl.ac.uk) running on MATLAB 7.9 (www.mathworks.com) on windows vista platform. No data were excluded from analysis because of excessive head motion (more than 3 mm in each direction). We used the VBM 5 toolbox (http://dbm.neuro.uni-jena.de) for image preprocessing, which included the following steps: normalization, segmentation, smoothing, and modulation. The VBM5 toolbox uses a new tissue-segmentation method implemented in SPM5. The details of the segmentation process performed in VBM5 toolbox have been described elsewhere (http://dbm.neuro.uni-jena.de/vbm/vbm5-for- spm5/manual/). To preserve the potential volume changes, the segmented images were modulated by the Jacobian determinants obtained from the spatial normalization procedure (Good et al. 2001). The analysis of the modulated gray and white matter segmented images can detect regional differences in the volumes of gray and white matter, respectively. Finally, the modulated gray matter images were smoothed with an isotropic Gaussian kernel with 8-mm full width–half maximum (FWHM) and analyzed statistically.

Statistical analysis

For group comparisons of demographic and clinical variables, the chi-square was used to analyze categorical variables and the Student's t-test for independent samples was employed for continuous measures. For gray and white matter volumes, the following analyses were performed: (1) group comparison of gray and white matter volumes in controls>depressed adolescents; (2) group comparison of gray and white matter volumes in depressed adolescents>controls. The analysis included grand mean scaling and absolute threshold masking (0.1). Subtraction analyses were employed to compare gray and white matter density in the two groups on a voxel-by-voxel basis using the two-sample t-test with SPM5. Significant group differences were estimated by the theory of random Gaussian fields, and alpha was set at 0.05 (family-wise error corrected), while the cluster size was set at >100 voxels displayed as statistical parametric maps into standard anatomical space (Zhou et al. 2009). In addition, an SPM5 regression analysis was conducted to examine the effect of SES and depression severity (based on total scores from HRSD/CDRS-R) on gray matter volume within each group separately as well as in the full sample. Furthermore, SES was employed as a covariate to examine its impact on gray matter differences between the two groups using SPM5 two-sample t-test.


Sample characteristics

Demographic and clinical characteristics of the sample are provided in Table 1. The groups did not differ significantly on any sociodemographic characteristics with the exception of SES (healthy adolescents had higher SES scores than depressed youth). As expected, depressed adolescents scored significantly higher on depression severity than controls. Out of 22 depressed adolescents, 2 met criteria for anxiety disorder and 3 for attention-deficit/hyperactivity disorder (ADHD). Based on FH-RDC, 52% of the depressed adolescents had family history of depression in their first-degree relatives. However, depressed adolescents with a family history of bipolar disorder in the first-degree relatives were excluded from the study. In addition, the healthy controls did not have any history of psychiatric illness in their first-degree relatives. No data were collected for the second-degree relatives for either group.

Table 1.
Demographic Characteristics of the Healthy and Depressed Adolescents

Gray matter volume

As shown in Table 2 and Figure 1, the VBM data illustrated that depressed adolescents had lower gray matter volume in bilateral dorsolateral prefrontal cortex (DLPFC) [including parts of right and left inferior and middle frontal gyri (MFG)], right OFC (BA 11), right frontopolar cortex (BA 10), right middle temporal gyrus (MTG), right superior temporal gyrus (STG), bilateral caudate, bilateral thalamus (pulvinar), and bilateral cerebellum (uvula and tonsil) than controls. In contrast to this, the controls had lower gray matter volume in the lentiform nucleus, globus pallidus, subgyral temporal lobe, and midbrain than the depressed group. The observed gray matter differences between the two groups lost statistical significance after covarying for SES. In addition, the gray matter volume did not correlate significantly with SES, CDRS-R, or HRSD scores. It is important to note that SES scores were missing for five controls and eight depressed adolescents, and exclusion of these subjects resulted in a reduced sample size for these analyses. One volunteer in the control group had an estimated IQ score of <70 but this subject had no evidence of mental retardation based on clinical evaluation, academic achievement scores, and psychosocial functioning. Exclusion of this participant from the analysis did not alter the results. There were no significant group differences in the white matter.

FIG. 1.
Cross hairs showing (A) orbitofrontal, (B) superior temporal gyrus, (C) caudate, and (D) dorsolateral with reduced gray matter volume in the depressed adolescents compared with controls. A color version of this figure is available in the online article ...
Table 2.
Whole-Brain Group Statistics for Healthy Controls>Depressed Adolescents


This study demonstrated significantly lower gray matter volume in the frontolimbic areas and caudate in depressed adolescents than controls. These brain regions are similar to those implicated in the psychopathology of MDD in both adolescents and adults in a number of prior neuroimaging studies (Merriam et al. 1999; Botteron et al. 2002; Bremner 2002; Frodl et al. 2004; Rajkowska et al. 2007; Steele et al. 2007; Drevets et al. 2008; Grimm et al. 2008; Zou et al. 2010; Peng et al. 2011). Although some of these studies used VBM to assess gray matter deficits in the prefrontal and limbic system in depressed adults (Frodl et al. 2004; Zou et al. 2010; Peng et al. 2011), to the best of our knowledge, the current study is the first VBM study to observe reduced gray matter volume in both prefrontal and limbic regions in adolescent depression.

The findings from this study should be interpreted with caution due to the modest sample sizes, which, along with missing SES data, could explain loss of significance in gray matter differences between the two groups after covariating for SES. Similar explanation can be given for lack of correlation between SES scores and gray matter volume in depressed adolescents in this study. It is worth mentioning that several investigations in animals and some human studies have demonstrated that adverse environmental conditions (which frequently coincide with low SES) can potentially induce structural and functional brain changes in youngsters and adults (Teicher et al. 2006). A subgroup of depressed adolescents had co-morbid anxiety disorders (n=2) and ADHD (n=3). Anxiety disorders and ADHD commonly co-occur in depressed adolescents and exclusion of participants with these co-morbid conditions would not have yielded a representative sample (Calles 2007).

Based on converging evidence from cognitive (Merriam et al. 1999), structural neuroimaging (Botteron et al. 2002; Bremner 2002; Steele et al. 2007; Drevets et al. 2008), functional neuroimaging (Drevets et al. 2008; Grimm et al. 2008), and postmortem (Rajkowska et al. 2007) studies, changes in PFC have been observed frequently in association with major depression. This is especially true for the DLPFC and OFC subregions of the PFC, which play a pivotal role in self-monitoring and decision making and provide executive control on other PFC regions, such as the medial PFC, as well as monitoring of the limbic system (MacDonald et al. 2000; Ridderinkhof et al. 2004). It is, however, important to mention that not all structural imaging studies have reported PFC changes in adolescent depression. For example, an earlier ROI-based study failed to observe a significant difference in OFC volume between depressed adolescents and controls (Chen et al. 2008). These conflicting results might be due to methodological differences between the VBM and ROI-based approaches.

Limbic regions have been frequently implicated in the pathophysiology of depression. Although neuroimaging studies have demonstrated functional and morphological changes in limbic structures (Drevets et al. 1997; Drevets 2001; Bremner 2002), and particularly the hippocampus (Bremner et al. 2000; Mervaala et al. 2000; Steffens et al. 2000; Sheline et al. 2002; Vythilingam et al. 2002; Hastings et al. 2004), in depressed adults (Ongür et al. 1998; Rajkowska et al. 1999; Cotter et al. 2001), it is not known whether similar findings also exist in the depressed adolescents. The current study is the first to report a reduction in gray matter volume in the superior temporal gyrus (STG; BA 38) and MTG (BA 21) in depressed adolescents. The STG is a complex brain region involved in the processing of sensory inputs to visceral emotional responses (Ding et al. 2009), and the MTG has been linked to decision making in the presence of stimulus conflict (Wendelken et al. 2009). Therefore, gray matter deficits in STG and MTG along with those in the DLPFC and OFC (also observed in this study) may provide the neuroanatomical basis for emotional dysregulation and impaired decision making in depressed adolescents, respectively (Cella et al. 2010).

In addition to the limbic system, several studies in depressed adults revealed gray matter deficits in the striatum, especially in the caudate nucleus (Krishnan et al. 1992; Krishnan et al. 1993; Parashos et al. 1998; Kim et al. 2008; Koolschijn et al. 2009; Pizzagalli et al. 2009), although not all studies replicated this finding (Greenwald et al. 1997; Pillay et al. 1998; Lacerda et al. 2003). Reduced gray matter volume in the caudate nucleus also was found in the current study, suggesting that changes in the striatum might occur in the initial presentation of MDD.

Gray matter deficits also were found in the thalamus, cerebellum, and frontopolar cortex in the depressed group. Although reduced thalamic volume was reported in adult women with MDD (Kim et al. 2008), this may be the first report of such deficits during early-onset depression. Interestingly, a postmortem study in depressed adults reported an increase in the number of neurons in the mediodorsal and anteroventral/anteromedial nuclei of the thalamus that connect subcortical limbic system structures (such as the amygdala) with the prefrontal and cingulate cortices (Young et al. 2004). Furthermore, an association between history of antidepressant use and reduction in the volume of various thalamic nuclei in depressed subjects (Young et al. 2008) suggests a potential antidepressant effect on the thalamus. In the current study, most of the depressed participants were never exposed to psychotropic agents and in the small subgroup (18%) that had ever been exposed to antidepressant agents, the duration of exposure was relatively brief (<2 months) and also not in close proximity to the MRI study. Therefore, medication exposure was unlikely to have a significant impact on brain structural changes.

With regards to the cerebellum, earlier studies documented decreased volume in the cerebellar vermis (Shah et al. 1998) and mean cerebellar volume (Escalona et al. 1993) in adults with MDD. A more recent study reported lower gray matter volume in the left cerebellum in patients with first-episode depression (Peng et al. 2011). These findings provide preliminary evidence that structural deficits in the cerebellum maybe incorporated in the neuroanatomical model of mood disorders.

Gray matter changes in the frontopolar region (BA 10), as observed in this study, have never been reported in any prior adolescent or adult studies. It has been proposed that this region is involved in the processing of “cognitive branching” (Koechlin and Hyafil 2007). Cognitive branching is a process through which memory of a previously ongoing task is maintained for subsequent retrieval and completion. Thus, gray matter deficits in this brain region in depressed adolescents may reflect a compromised ability for multitasking and scheduling operations.

Consistent with findings of the current study, an earlier VBM study failed to detect white matter deficits in depressed adults (Kim et al. 2008). Additionally, structural deficits in prefrontal regions, such as the subgenual anterior cingulate cortex (sgACC), frequently linked to depression in adults (Drevets et al. 1997; Hirayasu et al. 1999; Botteron et al. 2002; Hastings et al. 2004; Coryell et al. 2005; Drevets et al. 2008) and adolescents (Botteron et al. 2002), were not observed in this study. Since most of these studies were conducted in adults, the lack of gray matter deficits in the sgACC in depressed adolescents may be an age effect. It is also possible that this study may not have captured relatively small gray matter deficits, such as the sgACC, because the VBM analysis was thresholded at a large voxel size (i.e., >100 voxels). Also, this study failed to find gray matter deficits in the hippocampus, a brain region that implicated in adults (Campbell et al. 2004; Videbech and Ravnkilde 2004; Caetano et al. 2007) and adolescents with major depression (MacMaster and Kusumaker 2004; Rao et al. 2010). The reduced hippocampal volume associated with depression appears to be influenced by the number and duration of depressive episodes, suggesting greater variability in studies recruiting first-episode cases (Videbech and Ravnkilde 2004). On the other hand, hippocampal changes also maybe detected prior to the onset of illness in at-risk individuals (Macqueen and Frodl 2011; Rao et al. 2010).


Although several neuroimaging studies have revealed gray matter deficits in depressed adults, to our knowledge, this is the first report of gray matter changes in the frontolimbic areas and caudate in adolescent depression. These findings support the notion that structural deficits may be present at the onset of depressive illness. However, longitudinal studies in larger samples are required to confirm these results, controlling for the confounding effects of age, gender, SES, antidepressant pharmacotherapy, and duration of illness.

Clinical Significance

This, to our knowledge, is the first study to report gray matter deficits in frontolimbic and striatal cortex in depressed adolescents using voxel-based morphometry. These findings suggest that structural deficits may be present at the onset of depressive illness and may provide neuroanatomical markers for early depression.


Dr. Shad has received funding from Eli Lilly for other research. Drs. Muddasani and Rao have no financial conflicts of interest.


This work was supported, in part, by grants from the National Institutes of Health (DA14037, DA15131, DA17804, DA17805, MH62464, MH68391, RR003032 and RR026140). Sarah M. and Charles E. Seay are Endowed Chair in Child Psychiatry at UT Southwestern Medical Center, and the Endowed Chair in Brain and Behavior Research at Meharry Medical College.


  • Aalto-Setala T. Marttunen M. Tuulio-Henriksson A. Poikolainen K. Lonnqvist J. Depressive symptoms in adolescence as predictors of early adulthood depressive disorders and maladjustment. Am J Psychiatry. 2002;159:1235–1237. [PubMed]
  • Text revision (DSM-IV-TR) 4th. Washington, DC: American Psychiatric Association; 2000. American Psychiatric Association: Diagnostic, statistical manual of mental disorders.
  • Andreasen NC. Endicott J. Spitzer RL. Winokur G. The family history method using diagnostic criteria. Arch Gen Psychiatry. 1977;34:1229–1235. [PubMed]
  • Ashburner J. Friston KJ. Why voxel-based morphometry should be used. Neuroimage. 2001;14:1238–1243. [PubMed]
  • Ballmaier M. Kumar A. Thompson PM. Narr KL. Lavretsky H. Estanol H. Deluca H. Toga AW. Localizing grey matter deficits in late-onset depression using computational cortical pattern matching methods. Am J Psychiatry. 2004;161:2091–2099. [PubMed]
  • Birmaher B. Ryan ND. Williamson DE. Brent DA. Kaufman J. Childhood and adolescent depression: A review of the past ten years. Part I. J Am Acad Child Adolesc Psychiatry. 1996a;35:1427–1439. [PubMed]
  • Birmaher B. Ryan ND. Williamson DE. Brent DA. Kaufman J. Childhood and adolescent depression: A review of the past 10 years. Part II. J Am Acad Child Adolesc Psychiatry. 1996b;35:1575–1583. [PubMed]
  • Botteron KN. Raichle ME. Drevets WC. Heath AC. Todd RD. Volumetric reduction in left subgenual prefrontal cortex in early onset depression. Biol Psychiatry. 2002;51:342–344. [PubMed]
  • Bremner JD. Narayan M. Anderson ER. Staib LH. Miller HL. Charney DS. Hippocampal volume reduction in major depression. Am J Psychiatry. 2000;157:115–118. [PubMed]
  • Bremner JD. Structural changes in the brain in depression and relationship to symptom recurrence. CNS Spectr. 2002;7:129–130. [PubMed]
  • 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;427:142–147. [PubMed]
  • Calles JL:, Jr Depression in children and adolescents. Prim Care. 2007;34:243–258. [Review]. [PubMed]
  • Campbell S. Marriott M. Nahmias C. MacQueen GM. Lower hippocampal volume in patients suffering from depression: A meta-analysis. Am J Psychiatry. 2004;161:598–607. [PubMed]
  • Cella M. Dymond S. Cooper A. Impaired flexible decision-making in Major Depressive Disorder. J Affect Disord. 2010;124:207–210. [PubMed]
  • Chen H. 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 Psychopharmaco. 2008;18:551–556. [PubMed]
  • 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;162:1706–1712. [PubMed]
  • Cotter D. Mackay D. Landau S. Kerwin R. Everall I. Reduced glial cell density and neuronal size in the anterior cingulate cortex in major depressive disorder. Arch Gen Psychiatry. 2001;58:545–553. [PubMed]
  • Ding SL. Van Hoesen GW. Cassell MD. Poremba A. Parcellation of human temporal polar cortex: A combined analysis of multiple cytoarchitectonic, chemoarchitectonic, and pathological markers. J Comp Neurol. 2009;514:595–623. [PMC free article] [PubMed]
  • Drevets WC. Price JL. Furey ML. Brain structural and functional abnormalities in mood disorders: Implications for neurocircuitry models of depression. Brain Struct Funct. 2008;213:93–118. [PMC free article] [PubMed]
  • Drevets WC. Price JL. Simpson JR. Todd RD. Reich T. Vannier M. Raichle ME. Subgenual prefrontal cortex abnormalities in mood disorders. Nature. 1997;386:824–827. [PubMed]
  • Drevets WC. Neuroimaging and neuropathological studies of depression: Implications for the cognitive-emotional features of mood disorders. Curr Opin Neurobiol. 2001;11:240–249. [Review]. [PubMed]
  • Escalona PR. Early B. McDonald WM. Doraiswamy PM. Shah SA. Husain MM. Boyko OB. Figiel GS. Ellinwood EH. Nemeroff CB. Krishnan KRR. Reduction of cerebellar volume in major depression: A controlled MRI study. Depression. 1993;1:156–158.
  • Fergusson DM. Woodward LJ. Mental health, education, and social role outcomes of adolescents with depression. Arch Gen Psychiatry. 2002;59:225–231. [PubMed]
  • Frodl T. Meisenzahl EM. Zill P. Baghai T. Ruiescu D. Leinsinger G. Bottlender T. Schule C. Zwanzger P. Engel RR. Rupprecht R. Bondy B. Reiser M. Moller HJ. Reduced hippocampal volumes associated with the long variant of the serotonin transporter polymorphism in major depression. Arch Gen Psychiatry. 2004;61:177–183. [PubMed]
  • Garrison CZ. Addy CL. Jackson KL. McKeown RE. Waller JL. Major depressive disorder and dysthymia in young adolescents. Am J Epidemiol. 1992;135:792–802. [PubMed]
  • Good CD. Johnsrude IS. Ashburner J. Henson RN. Friston KJ. Frackowiak RS. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage. 2001;14:21–36. [PubMed]
  • Greenwald BS. Kramer-Ginsberg E. Bogerts B. Ashtari M. Aupperle P. Wu H. Allen L. Zeman D. Patel M. Qualitative magnetic resonance imaging findings in geriatric depression. Possible link between later-onset depression and Alzheimer's disease? Psychol Med. 1997;27:421–431. [PubMed]
  • Grimm S. Beck J. Schuepbach D. Hell D. Boesiger P. Bermpohl F. Niehaus L. Boeker H. Northoff G. Imbalance between left and right dorsolateral prefrontal cortex in major depression is linked to negative emotional judgment: An fMRI study in severe major depressive disorder. Biol Psychiatry. 2008;63:369–76. [PubMed]
  • Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;23:56–62. [PMC free article] [PubMed]
  • Hastings RS. Parsey RV. Oquendo MA. Arango V. Mann JJ. Volumetric analysis of the prefrontal cortex, amygdala, and hippocampus in major depression. Neuropsychopharmacology. 2004;29:952–9. [PubMed]
  • Hirayasu Y. Shenton ME. Salisbury DF. Kwon JS. Wible CG. Fischer IA. Yurgelun-Todd D. Zarate C. Kikinis R. Jolesz FA. McCarley RW. Subgenual cingulated cortex volume in first-episode psychosis. Am J Psychiatry. 1999;156:1091–1093. [PMC free article] [PubMed]
  • Hollingshead AB. Four Factor Index of Social Status. New Haven, CT: Yale University; 1975.
  • Kaufman J. Birmaher B. Brent D. Goldstein BI. Monk K. Kalas C. Kupfer D. Gill MK. Leibenluft E. Bridge J. Guyer A. Egger HL. Brent DA. Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): Initial reliability and validity data. J Am Acad Child Adolesc Psychiatry. 1997;36:980–988. [PubMed]
  • Kessler RC. Avenevoli S. Ries Merikangas K. Mood disorders in children and adolescents: An epidemiologic perspective. Biol Psychiatry. 2001;49:1002–1014. [PubMed]
  • Kim MJ. Hamilton JP. Gotlib IH. Reduced caudate gray matter volume in women with major depressive disorder. Psychiatry Res. 2008;164:114–122. [PMC free article] [PubMed]
  • Koechlin E. Hyafil A. Anterior prefrontal function and the limits of human-decision making. Science. 2007;318:594–598. [PubMed]
  • 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;30:3719–3735. [PubMed]
  • 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 Neurosc. 1993;243:41–46. [PubMed]
  • 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;49:553–557. [PubMed]
  • 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;124:129–140. [PubMed]
  • Lewinsohn PM. Hops H. Roberts RE. Seeley JR. Andrews JA. Adolescent psychopathology: I. Prevalence and incidence of depression and other DSM-III-R disorders in high school students. J Abnorm Psychol. 1993;102:133–144. [PubMed]
  • Li C. Sun X. Zou K. Yang H. Huang X. Wang Y. Lui S. Li D. Zou L. Chen H. Voxel based analysis of DTI in depression patients. Int J Mag Reson Imaging. 2007;1:043–048.
  • MacDonald AW., 3rd Cohen JD. Stenger VA. Carter CS. Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science. 2000;288:1835–1838. [PubMed]
  • MacMaster FP. Kusumakar V. Hippocampal volume in early onset depression. BMC Med. 2004;2:2. [PMC free article] [PubMed]
  • Macqueen G. Frodl T. The hippocampus in major depression: Evidence for the convergence of the bench and bedside in psychiatric research? Mol Psychiatry. 2011;16:252–264. [PubMed]
  • Marshall WA. Tanner JM. Variations in the pattern of pubertal changes in girls. Arch Dis Child. 1969;44:291–303. [PMC free article] [PubMed]
  • Marshall WA. Tanner JM. Variations in the pattern of pubertal changes in boys. Arch Dis Child. 1970;45:13–23. [PMC free article] [PubMed]
  • Merriam EP. Thase ME. Haas GL. Keshavan MS. Sweeney JA. Prefrontal Cortical Dysfunction in Depression Determined by Wisconsin Card Sorting Test Performance. Am J Psychiatry. 1999;156:780–782. [PubMed]
  • Mervaala E. Föhr J. Körnönen M. Vainio P. Partanen K. Partanen J. Tiihonen J. Viinamäki H. Karjalainen AK. Lehtonen J. Quantitative MRI of the hippocampus and amygdala in severe depression. Psychol Med. 2000;30:117–25. [PubMed]
  • San Francisco, CA: Author, University of California, San Francisco; 2006. National Adolescent Health Information Center: Fact Sheet on Suicide: Adolescents & Young Adults.
  • 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 Psychiatr. 2002;59:173–179. [PubMed]
  • Ongür D. Drevets WC. Price JL. Glial reduction in the subgenual prefrontal cortex in mood disorders. Proc Natl Acad Sci U S A. 1998;95:13290–13295. [PMC free article] [PubMed]
  • Parashos IA. Tupler LA. Blitchington T. Krishnan KR. Magnetic-resonance morphometry in patients with major depression. Psychiatry Res. 1998;84:7–15. [PubMed]
  • Peng J. Liu J. Nie B. Li Y. Shan B. Wang G. Li K. Cerebral and cerebellar gray matter reduction in first-episode patients with major depressive disorder: A voxel-based morphometry study. Eur J Radiol. 2011;80:395–399. [PubMed]
  • Pillay SS. Renshaw PF. Bonello CM. Lafer BC. Fava M. Yurgelun-Todd D. A quantitative magnetic resonance imaging study of caudate and lenticular nucleus grey matter volume in primary unipolar major depression: Relationship to treatment response and clinical severity. Psychiatry Res. 1998;84:61–74. [PubMed]
  • 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;166:702–710. [PMC free article] [PubMed]
  • Poznanski EO. Freman LN. Mokros HB. Children's depression rating scale-revised. Psychopharmacol Bull. 1985;21:979–989.
  • 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;45:1085–1098. [PubMed]
  • Rajkowska G. O'Dwyer G. Teleki Z. Stockmeier CA. Miguel-Hidalgo JJ. GABAergic neurons immunoreactive for calcium binding proteins are reduced in the prefrontal cortex in major depression. Neuropsychopharmacology. 2007;32:471–482. [PMC free article] [PubMed]
  • Rao U. Chen L-A. Bidesi AS. Shad MU. Thomas MA. Hammen CL. Hippocampal changes associated with early-life adversity and vulnerability to depression. Biol Psychiatry. 2010;67:357–364. [PMC free article] [PubMed]
  • Rao U. Chen LA. Characteristics, correlates, and outcomes of childhood and adolescent depressive disorders. Dialogues Clin Neurosci. 2009;11:45–62. [PMC free article] [PubMed]
  • Rao U. Hammen CL. Poland RE. Mechanisms underlying the comorbidity between depressive and addictive disorders in adolescents: Interactions between stress and HPA activity. Am J Psychiatry. 2009;166:361–369. [PMC free article] [PubMed]
  • Rao U. Weissman MM. Martin JA. Hammond RW. Childhood depression and risk of suicide: Preliminary report of a longitudinal study. J Am Acad Child Adolesc Psychiatry. 1993;32:21–27. [PubMed]
  • Ridderinkhof KR. Ullsperger M. Crone EA. Nieuwenhuis S. The Role of the Medial Frontal Cortex in Cognitive Control. Science. 2004;306:443–447. [PubMed]
  • Rushton JL. Forcier M. Schectman RM. Epidemiology of depressive symptoms in the National Longitudinal Study of Adolescent Health. J Am Acad Child Adolesc Psychiatry. 2002;41:199–205. [PubMed]
  • Shaffer D. Fisher P. Dulcan MK. Davies M. Piacentini J. Schwab-Stone ME. Lahey BB. Bourdon K. Jensen PS. Bird HR. Canino G. Regier DA. The NIMH Diagnostic Interview Schedule for Children Version 2.3 (DISC-2.3): Description, acceptability, prevalence rates, and performance in the MECA Study. Methods for the Epidemiology of Child and Adolescent Mental Disorders Study. J Am Acad Child Adolesc Psychiatry. 1996;35:865–877. [PubMed]
  • Shaffer D. Gould MS. Fisher P. Trautman P. Moreau D. Kleinman M. Flory M. Psychiatric diagnosis in child and adolescent suicide. Arch Gen Psychiatry. 1996;53:339–348. [PubMed]
  • 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. [PubMed]
  • Sheline YI. Mittler BL. Mintun MA. The hippocampus and depression. Eur Psychiatry. 2002;17(Suppl: 3):300–5. [Review]. [PubMed]
  • Soloff P. Nutche J. Goradia D. Diwadkar V. Structural brain abnormalities in borderline personality disorder: A voxel-based morphometry study. Psychiatry Res. 2008;164:223–36. [PMC free article] [PubMed]
  • Steele JD. Currie J. Lawrie SM. Reid I. Prefrontal cortical functional abnormality in major depressive disorder: A stereotactic meta-analysis. J Affect Disord. 2007;101:1–11. [PubMed]
  • Steffens DC. Byrum CE. McQuoid DR. Greenberg DL. Payne ME. Blitchington TF. MacFall JR. Krishnan KR. Hippocampal volume in geriatric depression. Biol Psychiatry. 2000;48:301–309. [PubMed]
  • 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;52:413–417. [PubMed]
  • Teicher MH. Tomoda A. Anderson SL. Neurobiological consequences of early stress and childhood maltreatment: Are results from human and animal studies comparable? Ann NY Acad Sci. 2006;1071:313–323. [PubMed]
  • Thompson WD. Orvaschel H. Prusoff BA. Kidd KK. An evaluation of the family history method for ascertaining psychiatric disorders. Arch Gen Psychiatry. 1982;39:53–58. [PubMed]
  • Vasic H. Walter Hose A. Wolf RC. Grey matter reduction associated with psychopathology and cognitive dysfunction in unipolar depression: A voxel-based morphometry study. J Affect Disord. 2008;109:107–116. [PubMed]
  • Videbech P. Ravnkilde B. Hippocampal volume and depression: A meta-analysis of MRI studies. Am J Psychiatry. 2004;161:1957–1966. [PubMed]
  • Vythilingam M. Heim C. Newport J. Vythilingam M. Heim C. Newport J. Miller AH. Anderson E. Bronen R. Brummer M. Staib L. Vermetten E. Charney DS. Nemeroff CB. Bremner JD. Childhood trauma associated with smaller hippocampal volume in women with major depression. Am J Psychiatry. 2002;159:2072–80. [PMC free article] [PubMed]
  • Wechsler D. San Antonio, TX: The Psychological Corporation; 1997. Wechsler Adult Intelligence Scale III (WAIS-III ®)
  • Wechsler D. 4th Edition. San Antonio, TX: Harcourt Assessment; 2003. Wechsler Intelligence Scale for Children. (WISC-IV ®)
  • Weissman MM. Wolk S. Goldstein RB. Moreau D. Adams P. Greenwald S. Klier CM. Ryan ND. Dahl RE. Wickramaratne P. Depressed adolescents grown up. JAMA. 1999;281:1707–1713. [PubMed]
  • Wendelken C. Ditterich J. Bunge SA. Carter CS. Stimulus and response conflict processing during perceptual decision making. Cogn Affect Behav Neurosci. 2009;9:434–447. [PubMed]
  • Whitaker A. Johnson J. Shaffer D. Rapoport JL. Kalikow K. Walsh BT. Davies M. Braiman S. Dolinsky A. Uncommon troubles in young people: Prevalence estimates of selected psychiatric disorders in a nonreferred adolescent population. Arch Gen Psychiatry. 1990;47:487–496. [PubMed]
  • Young KA. Bonkale WL. Holcomb LA. Hicks PB. German DC. Major depression, 5HTTLPR genotype, suicide and antidepressant influences on thalamic volume. Br J Psychiatry. 2008;192:285–289. [PubMed]
  • Young KA. Holcomb LA. Yazdani U. Hicks PB. German DC. Elevated neuron number in the limbic thalamus in major depression. Am J Psychiatry. 2004;61:1270–1277. [PubMed]
  • Zhou Y. Lin FC. Du YS. Qin LD. Zhao ZM. Xu JR. Lei H. Gray matter abnormalities in Internet addiction: A voxel-based morphometry study. Eur J Radiol. 2009;79:92–95. [PubMed]
  • Zhou Y. Yu C. Zheng H. Liu Y. Song M. Qin W. Li K. Jiang T. Increased neural resources recruitment in the intrinsic organization in major depression. J Affect Disord. 2010;121:220–230. [PubMed]
  • 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;67:186–188. [PubMed]

Articles from Journal of Child and Adolescent Psychopharmacology are provided here courtesy of Mary Ann Liebert, Inc.
PubReader format: click here to try


Save items

Related citations in PubMed

See reviews...See all...

Cited by other articles in PMC

See all...


  • PubMed
    PubMed citations for these articles

Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...