Search

Objectives: Identifying hubs of brain dysfunction in adolescents and young adults with Bipolar I Disorder (BDAYA) could provide targets for early detection, prevention, and treatment. Previous neuroimaging studies across mood states of BDAYA are scarce and often examined limited brain regions potentially prohibiting detection of other important regions. We used a data‐driven whole‐brain Intrinsic Connectivity Distribution (ICD) approach to investigate dysconnectivity hubs across mood states in BDAYA. Methods: Functional magnetic resonance imaging whole‐brain ICD data were investigated for differences across four groups: BDAYA‐depressed (n = 22), BDAYA‐euthymic (n = 45), BDAYA‐elevated (n = 24), and healthy controls (HC, n = 111). Clusters of ICD differences were assessed for regional dysconnectivity and mood symptom relationships. Analyses were also performed for BDAYA overall (vs. HC) ICD differences persisting across mood states. Results: ICD was higher in the BDAYA‐ depressed group than other groups in bilateral ventral/rostral/dorsal prefrontal cortex (PFC) and right lenticular nucleus (LN) (pcorrected <0.05). In BDAYA‐depressed, functional connectivity (FC) was increased between these regions with their contralateral homologues and PFC‐medial temporal FC was more negative (p < 0.005). PFC‐related findings correlated with depression scores (p < 0.05). The overall BDAYA group showed ICD increases in more ventral left PFC and right cerebellum, present across euthymia and acute mood states. Conclusions: This ICD approach supports a PFC hub of inter‐ and intra‐hemispheric frontotemporal dysconnectivity in BDAYA with potential trait features and disturbances of higher magnitude during depression. Hubs were also revealed in LN and cerebellum, less common foci of BD research. The hubs are potential targets for early interventions to detect, prevent, and treat BD. [ABSTRACT FROM AUTHOR]

Objectives Recent studies have indicated that pattern recognition techniques of functional magnetic resonance imaging ( fMRI) data for individual classification may be valuable for distinguishing between major depressive disorder ( MDD) and bipolar disorder ( BD). Importantly, medication may have affected previous classification results as subjects with MDD and BD use different classes of medication. Furthermore, almost all studies have investigated only depressed subjects. Therefore, we focused on medication-free subjects. We additionally investigated whether classification would be mood state independent by including depressed and remitted subjects alike. Methods We applied Gaussian process classifiers to investigate the discriminatory power of structural MRI (gray matter volumes of emotion regulation areas) and resting-state fMRI (resting-state networks implicated in mood disorders: default mode network [ DMN], salience network [ SN], and lateralized frontoparietal networks [ FPNs]) in depressed (n=42) and remitted (n=49) medication-free subjects with MDD and BD. Results Depressed subjects with MDD and BD could be classified based on the gray matter volumes of emotion regulation areas as well as DMN functional connectivity with 69.1% prediction accuracy. Prediction accuracy using the FPNs and SN did not exceed chance level. It was not possible to discriminate between remitted subjects with MDD and BD. Conclusions For the first time, we showed that medication-free subjects with MDD and BD can be differentiated based on structural MRI as well as resting-state functional connectivity. Importantly, the results indicated that research concerning diagnostic neuroimaging tools distinguishing between MDD and BD should consider mood state as only depressed subjects with MDD and BD could be correctly classified. Future studies, in larger samples are needed to investigate whether the results can be generalized to medication-naïve or first-episode subjects. [ABSTRACT FROM AUTHOR]

Objectives Bipolar disorder ( BD) is associated with significant impairment in cognitive performance across multiple domains of function that often persist after clinical recovery. It remains unclear, however, as to whether this process is related to the clinical status of BD being depressed, manic/hypomanic, or euthymic. In this review, we examine the literature on the cross-sectional and longitudinal relationships between cognitive function and general function depending on the clinical phase of BD. Methods A systematic review of original research that studied both cognitive function and general function in adults (18-60 years), restricted to BD, was conducted in a total of 18 studies meeting inclusion/exclusion criteria. Results Results show cross-sectional and prospective relationships between cognitive function and general function in patients with BD in both symptomatic and euthymic patients with BD. While studies using general measures of function (e.g., Global Assessment of Function scale) show more inconsistent associations with cognitive function, those employing assessments of domain specific function, suggest a consistent relationship between social and occupational function and cognitive performance. Executive function is commonly affected by cognitive deficits in these patients, but in addition a variety of domains show associations with functional outcomes (e.g., social function, occupational function). Notably, the emerging evidence suggests that cognitive function may be a better predictor of future general function than affective symptom severity. Conclusions Despite some inconsistencies, in sum the literature on the relationship between cognitive function and general function in BD implicates both cross-sectional and longitudinal associations, both in symptomatic and euthymic patients with BD. And in terms of capturing these changes functional scales in particular domain-specific measures seem superior to general measures. [ABSTRACT FROM AUTHOR]