Divergent thinking is a critical creative cognitive process. Its neural mechanisms have been well-studied through structural and functional imaging in healthy individuals but are less explored in patients with bipolar disorder (BD). Because of the traditional link between creativity and BD, this study investigated the structural correlates of divergent thinking in patients with BD through surface-based morphometry.

Fifty-nine patients diagnosed with BD I or BD II (35.3 ± 8.5 years) and 56 age- and sex-matched controls (33.9 ± 7.4 years) were recruited. The participants underwent structural magnetic resonance imaging and an evaluation of divergent thinking by using the Chinese version of the Abbreviated Torrance Test for Adults (ATTA). FreeSurfer 7.0 was used to generate thickness and surface area maps for each participant. Brainwise regression of the association between cortical thickness or surface area and ATTA performance was conducted using general linear models.

Divergent thinking performance did not differ significantly between the patients with BD and the healthy controls. In these patients, total ATTA score was negatively correlated with cortical thickness in the right middle frontal gyrus, right occipital, and left precuneus but positively correlated with the surface area of the right superior frontal gyrus. By contrast, total ATTA scores and cortical thickness or surface area were not significantly correlated among the controls.

The findings indicate that divergent thinking involves cerebral structures for executive control, mental imagery, and visual processing in patients with BD, and the right prefrontal cortex might be the most crucial of these structures.

This study aims to gain insight into each attribute as presented in the value of implantable medical devices, quantify attributes’ strength and their relative importance, and identify the determinants of stakeholders’ preferences.

A mixed-methods design was used to identify attributes and levels reflecting stakeholders’ preference toward the value of implantable medical devices. This design combined literature reviewing, expert’s consultation, one-on-one interactions with stakeholders, and a pilot testing. Based on the design, six attributes and their levels were settled. Among 144 hypothetical profiles, 30 optimal choice sets were developed, and healthcare professionals (decision-makers, health technology assessment experts, hospital administrators, medical doctors) and patients as stakeholders in China were surveyed. A total of 134 respondents participated in the survey. Results were analyzed by mixed logit model and conditional logit model.

The results of the mixed logit model showed that all the six attributes had a significant impact on respondents’ choices on implantable medical devices. Respondents were willing to pay the highest for medical devices that provided improvements in clinical safety, followed by increased clinical effectiveness, technology for treating severe diseases, improved implement capacity, and innovative technology (without substitutes).

The findings of DCE will improve the current evaluation on the value of implantable medical devices in China and provide decision-makers with the relative importance of the criteria in pricing and reimbursement decision-making of implantable medical devices.

Support vector machines (SVMs) based on brain-wise functional connectivity (FC) have been widely adopted for single-subject prediction of patients with schizophrenia, but most of them had small sample size. This study aimed to evaluate the performance of SVMs based on a large single-site dataset and investigate the effects of demographic homogeneity and training sample size on classification accuracy.

The resting functional Magnetic Resonance Imaging (fMRI) dataset comprised 220 patients with schizophrenia and 220 healthy controls. Brain-wise FCs was calculated for each participant and linear SVMs were developed for automatic classification of patients and controls. First, we evaluated the SVMs based on all participants and homogeneous subsamples of men, women, younger (18–30 years), and older (31–50 years) participants by 10-fold nested cross-validation. Then, we hold out a fixed test set of 40 participants (20 patients and 20 controls) and evaluated the SVMs based on incremental training sample sizes (N = 40, 80, …, 400).

We found that the SVMs based on all participants had accuracy of 85.05%. The SVMs based on male, female, young, and older participants yielded accuracy of 84.66, 81.56, 80.50, and 86.13%, respectively. Although the SVMs based on older subsamples had better performance than those based on all participants, they generalized poorly to younger participants (77.24%). For incremental training sizes, the classification accuracy increased stepwise from 72.6 to 83.3%, with >80% accuracy achieved with sample size >240.

The findings indicate that SVMs based on a large dataset yield high classification accuracy and establish models using a large sample size with heterogeneous properties are recommended for single subject prediction of schizophrenia.

Recent imaging studies of large datasets suggested that psychiatric disorders have common biological substrates. This study aimed to identify all the common neural substrates with connectomic abnormalities across four major psychiatric disorders by using the data-driven connectome-wide association method of multivariate distance matrix regression (MDMR).

This study analyzed a resting functional magnetic resonance imaging dataset of 100 patients with schizophrenia, 100 patients with bipolar I disorder, 100 patients with bipolar II disorder, 100 patients with major depressive disorder, and 100 healthy controls (HCs). We calculated a voxel-wise 4,330 × 4,330 matrix of whole-brain functional connectivity (FC) with 8-mm isotropic resolution for each participant and then performed MDMR to identify structures where the overall multivariate pattern of FC was significantly different between each patient group and the HC group. A conjunction analysis was performed to identify common neural regions with FC abnormalities across these four psychiatric disorders.

The conjunction of the MDMR maps revealed that the four groups of patients shared connectomic abnormalities in distributed cortical and subcortical structures, which included bilateral thalamus, cerebellum, frontal pole, supramarginal gyrus, postcentral gyrus, lingual gyrus, lateral occipital cortex, and parahippocampus. The follow-up analysis based on pair-wise FC of these regions demonstrated that these psychiatric disorders also shared similar patterns of FC abnormalities characterized by sensory/subcortical hyperconnectivity, association/subcortical hypoconnectivity, and sensory/association hyperconnectivity.

These findings suggest that major psychiatric disorders share common connectomic abnormalities in distributed cortical and subcortical regions and provide crucial support for the common network hypothesis of major psychiatric disorders.

The condition of caregivers is important to the quality of care received by people with Parkinson’s disease (PD), especially at the late disease stages. This study addresses the distress placed on caregivers by participants’ neuropsychiatric symptoms at different stages of PD in Taiwan

This prospective study enrolled 108 people with PD. All participants were examined with the Unified Parkinson’s Disease Rating Scale (UPDRS), Neuropsychiatric Inventory (NPI), Mini-Mental State Examination (MMSE), Cognitive Abilities Screening Instrument (CASI), and Clinical Dementia Rating (CDR) scale. Caregiver distress was measured using the Neuropsychiatric Inventory Caregiver Distress Scale (NPI-D). Statistical analysis was used to explore the PD-related factors that contribute to caregiver distress.

The mean follow-up interval in the 108 PD participants were 24.0 ± 10.2 months with no participant lost to follow-up due to death. NPI-distress (the sum of NPI caregiver distress scale across the 12 domains of the NPI) was positively correlated with NPI-sum (the total score across the 12 domains of the NPI) (r = 0.787, p < 0.001), CDR (r = 0.403, p < 0.001), UPRDS (r = 0.276, p = 0.004), and disease duration (r = 0.246, p = 0.002), but negatively correlated with CASI (r = −0.237, p = 0.043) and MMSE (r = −0.281, p < 0.001). Multiple linear regression analysis showed that only NPI-sum and disease duration were independently correlated with NPI-distress.

The disease duration and NPI-sum are independent predictors of caregiver distress in Taiwanese populations with PD. Early detection and reduction of neuropsychiatric symptoms in people with PD can help decrease caregiver distress.