Background: Bipolar disorder (BD) is a chronic psychiatric mood disorder that is solely diagnosed based on clinical symptoms. These symptoms often overlap with other psychiatric disorders. Efforts to use machine learning (ML) to create predictive models for BD based on data from brain imaging are expanding but have often been limited using only a single modality and the exclusion of the cerebellum, which may be relevant in BD.
View Article and Find Full Text PDFBackground: Widely reported by bipolar disorder (BD) patients, cognitive symptoms, including deficits in executive function, memory, attention, and timing are under-studied. Work suggests that individuals with BD show impairments in interval timing tasks, including supra-second, sub-second, and implicit motor timing compared to the neuronormative population. However, how time perception differs within individuals with BD based on disorder sub-type (BDI vs II), depressed mood, or antipsychotic medication-use has not been thoroughly investigated.
View Article and Find Full Text PDFBackground: The neural underpinnings of bipolar disorder (BD) remain poorly understood. The cerebellum is ideally positioned to modulate emotional regulation circuitry yet has been understudied in BD. Literature suggests differences in cerebellar activity and metabolism in BD, however findings on structural differences remain contradictory.
View Article and Find Full Text PDF: Widely reported by bipolar disorder (BD) patients, cognitive symptoms, including deficits in executive function, memory, attention, and timing are under-studied. Work suggests that individuals with BD show impairments in interval timing tasks, including supra-second, sub-second, and implicit motor timing compared to the neuronormative population. However, how time perception differs within individuals with BD based on BD sub-type (BDI vs II), mood, or antipsychotic medication-use has not been thoroughly investigated.
View Article and Find Full Text PDFPurpose: Studies of the neural underpinnings of bipolar type I disorder have focused on the emotional control network. However, there is also growing evidence for cerebellar involvement, including abnormal structure, function, and metabolism. Here, we sought to assess functional connectivity of the cerebellar vermis with the cerebrum in bipolar disorder and to assess whether connectivity might depend on mood.
View Article and Find Full Text PDFPurpose: Studies of the neural underpinnings of bipolar type I disorder have focused on the emotional control network. However, there is also growing evidence for cerebellar involvement, including abnormal structure, function, and metabolism. Here, we sought to assess functional connectivity of the cerebellum with the cerebrum in bipolar disorder and to assess whether any effects might depend on mood.
View Article and Find Full Text PDFObjective: Circadian rhythm disruption is commonly observed in bipolar disorder (BD). Daylight is the most powerful signal to entrain the human circadian clock system. This exploratory study investigated if solar insolation at the onset location was associated with the polarity of the first episode of BD I.
View Article and Find Full Text PDFAims: Bipolar type I disorder (BD) is characterized by severe mood swings and occurs in about 1% of the population. The mechanisms underlying the disorder remain unknown. Prior studies have suggested abnormalities in brain metabolism using H and P magnetic resonance spectroscopy (MRS).
View Article and Find Full Text PDFSimilar to biological mothers during the postpartum period, women who adopt children experience increased stress and life changes that may put them at risk for developing depression and anxiety. The purpose of the current study was to compare levels of depression and anxiety symptoms between postpartum and adoptive women and, among adoptive women, to examine associations between specific stressors and depressive symptoms. Data from adoptive mothers (n = 147), recruited from Holt International, were compared to existing data from postpartum women (n = 147).
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