Background: Subcortical volumetric abnormalities in schizophrenia, bipolar disorder and major depressive disorder (MDD) have been consistently found on a single-diagnosis basis in previous studies. However, whether such volumetric abnormalities are specific to a particular disorder or shared by other disorders remains unclear.
Methods: We analyzed the structural MRIs of 160 patients with schizophrenia, 160 patients with bipolar disorder, 160 patients with MDD and 160 healthy controls. We calculated the volumes of the thalamus, hippocampus, amygdala, accumbens, putamen, caudate, pallidum and lateral ventricles using FreeSurfer 7.0 and compared them among the groups using general linear models.
Results: We found a significant group effect on the volumes of the thalamus, hippocampus, accumbens and pallidum. Further post hoc analysis revealed that thalamic volumes in patients with schizophrenia, bipolar disorder and MDD were significantly reduced compared to those in healthy controls, but did not differ from one another. Patients with schizophrenia and bipolar disorder also shared a significant reduction in hippocampal volumes. Among the 3 clinical groups, patients with schizophrenia showed significantly lower hippocampal volumes and higher pallidal volumes than patients with bipolar disorder and MDD.
Limitations: Differences in psychotropic use and duration of illness among the patient groups may limit the interpretation of our findings.
Conclusion: Our findings indicate that decreased thalamic volume is a common feature of schizophrenia, bipolar disorder and MDD. Smaller hippocampal and larger pallidal volumes differentiate schizophrenia from bipolar disorder and MDD and may provide clues to the biological basis for the Kraepelinian distinction between these illnesses.
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http://dx.doi.org/10.1503/jpn.210154 | DOI Listing |
Dev Neurobiol
January 2025
Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, Massachusetts, USA.
The term "neurodiversity" refers to the natural heterogeneity in human neurological functioning, which includes neurodevelopmental differences and other mental health conditions (e.g., autism spectrum disorder [ASD], attention-deficit hyperactivity disorder [ADHD], dyslexia, bipolar disorder, schizophrenia, and depression).
View Article and Find Full Text PDFBrain Imaging Behav
January 2025
Key Laboratory of Adolescent Cyberpsychology and Behavior (Ministry of Education), Wuhan, China.
Bipolar disorder (BD) is a complex psychiatric condition marked by significant mood fluctuations that deeply affect quality of life. Understanding the neural mechanisms underlying BD is critical for improving diagnostic accuracy and developing more effective treatments. This study utilized resting-state functional magnetic resonance imaging (rs-fMRI) to investigate functional connectivity within the ventral and dorsal attention networks in 52 patients with BD and 51 healthy controls.
View Article and Find Full Text PDFCureus
December 2024
Department of Psychiatry, Tokyo Women's Medical University, Tokyo, JPN.
Introduction Psychoeducation is a form of psychosocial treatment with proven efficacy in preventing the relapse of bipolar disorder (BD). However, the effectiveness of psychoeducation has not been verified in Japan. We aimed to examine the effect of a brief group psychoeducation course (eight-session long) on relapse prevention in Japanese patients with BD and associated factors.
View Article and Find Full Text PDFAnn Gen Psychiatry
January 2025
AbbVie, North Chicago, IL, USA.
Background: Atypical antipsychotics are a common treatment for serious mental illness, but many are associated with adverse effects, including weight gain and cardiovascular issues, and real-world experience may differ from clinical trial data. Cariprazine has previously demonstrated a favorable safety and tolerability profile in clinical trials. Here, we evaluated the effects of cariprazine on body weight and blood pressure for bipolar I disorder (BP-I), schizophrenia, or as adjunctive treatment for major depressive disorder (MDD) using real-world data.
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