Christine Konradi, PhD; Eric I. Zimmerman, BS; C. Kevin Yang, BS; et al.
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Arch Gen Psychiatry. 2011;68(4):340-350. doi:10.1001/archgenpsychiatry.2010.175
ContextPostmortem studies have reported decreased density and decreased gene expression of hippocampal interneurons in bipolar disorder, but neuroimaging studies of hippocampal volume and function have been inconclusive.ObjectiveTo assess hippocampal volume, neuron number, and interneurons in the same specimens of subjects with bipolar disorder and healthy control subjects.DesignWhole human hippocampi of 14 subjects with bipolar disorder and 18 healthy control subjects were cut at 2.5-mm intervals and sections from each tissue block were either Nissl-stained or stained with antibodies against somatostatin or parvalbumin. Messenger RNA was extracted from fixed tissue and real-time quantitative polymerase chain reaction was performed.SettingBasic research laboratories at Vanderbilt University and McLean Hospital.SamplesBrain specimens from the Harvard Brain Tissue Resource Center at McLean Hospital.Main Outcome MeasuresVolume of pyramidal and nonpyramidal cell layers, overall neuron number and size, number of somatostatin- and parvalbumin-positive interneurons, and messenger RNA levels of somatostatin, parvalbumin, and glutamic acid decarboxylase 1.ResultsThe 2 groups did not differ in the total number of hippocampal neurons, but the bipolar disorder group showed reduced volume of the nonpyramidal cell layers, reduced somal volume in cornu ammonis sector 2/3, reduced number of somatostatin- and parvalbumin-positive neurons, and reduced messenger RNA levels for somatostatin, parvalbumin, and glutamic acid decarboxylase 1.ConclusionOur results indicate a specific alteration of hippocampal interneurons in bipolar disorder, likely resulting in hippocampal dysfunction.
Roy H. Perlis, MD, MSc; Rudolf Uher, PhD, MRCPsych; Michael Ostacher, MD; et al.
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Arch Gen Psychiatry. 2011;68(4):351-360. doi:10.1001/archgenpsychiatry.2010.179
ContextIt has been suggested that patients with major depressive disorder (MDD) who display pretreatment features suggestive of bipolar disorder or bipolar spectrum features might have poorer treatment outcomes.ObjectiveTo assess the association between bipolar spectrum features and antidepressant treatment outcome in MDD.DesignOpen treatment followed by sequential randomized controlled trials.SettingPrimary and specialty psychiatric outpatient centers in the United States.ParticipantsMale and female outpatients aged 18 to 75 years with a DSM-IV diagnosis of nonpsychotic MDD who participated in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study.InterventionsOpen treatment with citalopram followed by up to 3 sequential next-step treatments.Main Outcome MeasuresNumber of treatment levels required to reach protocol-defined remission, as well as failure to return for the postbaseline visit, loss to follow-up, and psychiatric adverse events. For this secondary analysis, putative bipolar spectrum features, including items on the mania and psychosis subscales of the Psychiatric Diagnosis Screening Questionnaire, were examined for association with treatment outcomes.ResultsOf the 4041 subjects who entered the study, 1198 (30.0%) endorsed at least 1 item on the psychosis scale and 1524 (38.1%) described at least 1 recent maniclike/hypomaniclike symptom. Irritability and psychoticlike symptoms at entry were significantly associated with poorer outcomes across up to 4 treatment levels, as were shorter episodes and some neurovegetative symptoms of depression. However, other indicators of bipolar diathesis including recent maniclike symptoms and family history of bipolar disorder as well as summary measures of bipolar spectrum features were not associated with treatment resistance.ConclusionSelf-reported psychoticlike symptoms were common in a community sample of outpatients with MDD and strongly associated with poorer outcomes. Overall, the data do not support the hypothesis that unrecognized bipolar spectrum illness contributes substantially to antidepressant treatment resistance.
Tim Hahn, PhD; Andre F. Marquand, MSc; Ann-Christine Ehlis, PhD; et al.
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Arch Gen Psychiatry. 2011;68(4):361-368. doi:10.1001/archgenpsychiatry.2010.178
ContextAlthough psychiatric disorders are, to date, diagnosed on the basis of behavioral symptoms and course of illness, the interest in neurobiological markers of psychiatric disorders has grown substantially in recent years. However, current classification approaches are mainly based on data from a single biomarker, making it difficult to predict disorders characterized by complex patterns of symptoms.ObjectiveTo integrate neuroimaging data associated with multiple symptom-related neural processes and demonstrate their utility in the context of depression by deriving a predictive model of brain activation.DesignTwo groups of participants underwent functional magnetic resonance imaging during 3 tasks probing neural processes relevant to depression.SettingParticipants were recruited from the local population by use of advertisements; participants with depression were inpatients from the Department of Psychiatry, Psychosomatics, and Psychotherapy at the University of Wuerzburg, Wuerzburg, Germany.ParticipantsWe matched a sample of 30 medicated, unselected patients with depression by age, sex, smoking status, and handedness with 30 healthy volunteers.Main Outcome MeasureAccuracy of single-subject classification based on whole-brain patterns of neural responses from all 3 tasks.ResultsIntegrating data associated with emotional and affective processing substantially increases classification accuracy compared with single classifiers. The predictive model identifies a combination of neural responses to neutral faces, large rewards, and safety cues as nonredundant predictors of depression. Regions of the brain associated with overall classification comprise a complex pattern of areas involved in emotional processing and the analysis of stimulus features.ConclusionsOur method of integrating neuroimaging data associated with multiple, symptom-related neural processes can provide a highly accurate algorithm for classification. The integrated biomarker model shows that data associated with both emotional and reward processing are essential for a highly accurate classification of depression. In the future, large-scale studies will need to be conducted to determine the practical applicability of our algorithm as a biomarker-based diagnostic aid.
Lars T. Westlye, PhD; Astrid Bjørnebekk, PhD; Håkon Grydeland, MA; et al.
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Arch Gen Psychiatry. 2011;68(4):369-377. doi:10.1001/archgenpsychiatry.2011.24
Michelle G. Craske, PhD; Murray B. Stein, MD, MPH; Greer Sullivan, MD; et al.
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Arch Gen Psychiatry. 2011;68(4):378-388. doi:10.1001/archgenpsychiatry.2011.25
Andrea C. King, PhD; Harriet de Wit, PhD; Patrick J. McNamara, BS; et al.
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Arch Gen Psychiatry. 2011;68(4):389-399. doi:10.1001/archgenpsychiatry.2011.26
Laura D. Kubzansky, PhD; Nansook Park, PhD; Christopher Peterson, PhD; et al.
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Arch Gen Psychiatry. 2011;68(4):400-408. doi:10.1001/archgenpsychiatry.2011.23
Jitender Sareen, MD, FRCPC; Tracie O. Afifi, PhD; Katherine A. McMillan, MS; et al.
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Arch Gen Psychiatry. 2011;68(4):419-427. doi:10.1001/archgenpsychiatry.2011.15
Joshua Breslau, PhD, ScD; Guilherme Borges, PhD; Daniel Tancredi, PhD; et al.
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Arch Gen Psychiatry. 2011;68(4):428-433. doi:10.1001/archgenpsychiatry.2011.21