It has been suggested that schizophrenia involves dysconnectivity between functional brain regions and also the white matter structural disorganisation. Thus, diffusion tensor imaging (DTI) has widely been used for studying schizophrenia. However, most previous studies have used the region of interest (ROI) based approach. We, therefore, performed the probabilistic tractography method in this study to reveal the alterations of white matter tracts in the schizophrenia brain.

A total of four different datasets consisted of 189 patients with schizophrenia and 213 healthy controls were investigated. We performed retrospective harmonisation of raw diffusion MRI data by dMRIharmonisation and used the FMRIB Software Library (FSL) for probabilistic tractography. The connectivities between different ROIs were then compared between patients and controls. Furthermore, we evaluated the relationship between the connection probabilities and the symptoms and cognitive measures in patients with schizophrenia.

After applying Bonferroni correction for multiple comparisons, 11 different tracts showed significant differences between patients with schizophrenia and healthy controls. Many of these tracts were associated with the basal ganglia or cortico-striatal structures, which aligns with the current literature highlighting striatal dysfunction. Moreover, we found that these tracts demonstrated statistically significant relationships with few cognitive measures related to language, executive function, or processing speed.

We performed probabilistic tractography using a large, harmonised dataset of diffusion MRI data, which enhanced the statistical power of our study. It is important to note that most of the tracts identified in this study, particularly callosal and cortico-striatal streamlines, have been previously implicated in schizophrenia within the current literature. Further research with harmonised data focusing specifically on these brain regions could be recommended.

Previous analyses of grey and white matter volumes have reported that schizophrenia is associated with structural changes. Deep learning is a data-driven approach that can capture highly compact hierarchical non-linear relationships among high-dimensional features, and therefore can facilitate the development of clinical tools for making a more accurate and earlier diagnosis of schizophrenia.

To identify consistent grey matter abnormalities in patients with schizophrenia, 662 people with schizophrenia and 613 healthy controls were recruited from eight centres across China, and the data from these independent sites were used to validate deep-learning classifiers.

We used a prospective image-based meta-analysis of whole-brain voxel-based morphometry. We also automatically differentiated patients with schizophrenia from healthy controls using combined grey matter, white matter and cerebrospinal fluid volumetric features, incorporated a deep neural network approach on an individual basis, and tested the generalisability of the classification models using independent validation sites.

We found that statistically reliable schizophrenia-related grey matter abnormalities primarily occurred in regions that included the superior temporal gyrus extending to the temporal pole, insular cortex, orbital and middle frontal cortices, middle cingulum and thalamus. Evaluated using leave-one-site-out cross-validation, the performance of the classification of schizophrenia achieved by our findings from eight independent research sites were: accuracy, 77.19–85.74%; sensitivity, 75.31–89.29% and area under the receiver operating characteristic curve, 0.797–0.909.

These results suggest that, by using deep-learning techniques, multidimensional neuroanatomical changes in schizophrenia are capable of robustly discriminating patients with schizophrenia from healthy controls, findings which could facilitate clinical diagnosis and treatment in schizophrenia.

Caregiver satisfaction and experience surveys help health professionals to understand, measure, and improve the quality of care provided for patients and their families.

Our aim was to explore caregiver perceptions of the care received from Australian specialist palliative care services.

Caregivers of patients receiving palliative care in services registered with Australia's Palliative Care Outcomes Collaboration were invited to participate in a caregiver survey. The survey included the FAMCARE–2 and four items from the Ongoing Needs Identification: Caregiver Profile questionnaire.

Surveys were completed by 1,592 caregivers from 49 services. Most respondents reported high satisfaction and positive experiences. Caregivers receiving care from community-based palliative care teams were less satisfied with the management of physical symptoms and comfort (odds ratio [OR] = 0.29; 95% confidence interval [CI95%] = 0.14, 0.59), with patient psychological care (OR = 0.56; CI95% = 0.32, 0.98), and with family support (OR = 0.52; CI95% = 0.35, 0.77) than caregivers of patients in an inpatient setting. If aged over 60 years, caregivers were less likely to have their information needs met regarding available support services (OR = 0.98; CI95% = 0.97, 0.98) and carer payments (OR = 0.99; CI95% = 0.98, 1.00). Also, caregivers were less likely to receive adequate information about carer payments if located in an outer regional area (OR = 0.41; CI95% = 0.25, 0.64). With practical training, caregivers receiving care from community services reported inadequate information provision to support them in caring for patients (OR = 0.60; CI95% = 0.45, 0.81).

While our study identified caregivers as having positive and satisfactory experiences across all domains of care, there is room for improvement in the delivery of palliative care across symptom management, as well as patient and caregiver support, especially in community settings. Caregiver surveys can facilitate the identification and evaluation of both patients' and caregivers' experiences, satisfaction, distress, and unmet needs.