Effects of magnanimous therapy on coping, adjustment, and living function in advanced lung cancer.

Objectives: We examined the effects of magnanimous therapy on psychological coping, adjustment, living function, and survival rate in patients with advanced lung cancer.

Methods: Patients with advanced lung cancer ( = 145) matched by demographics and medical variables were randomly assigned to an individual computer magnanimous therapy group (ic-mt), a group computer magnanimous therapy group (gc-mt), or a control group (ctrl). Over 2 weeks, the ic-mt and gc-mt groups received eight 40-minute sessions of ic-mt or gc-mt respectively, plus usual care; the ctrl group received only usual care.

Abnormal Global Functional Connectivity Patterns in Medication-Free Major Depressive Disorder.

Mounting studies have applied resting-state functional magnetic resonance imaging (rs-fMRI) to study major depressive disorder (MDD) and have identified abnormal functional activities. However, how the global functional connectivity patterns change in MDD is still unknown. Using rs-fMRI, we investigated the alterations of global resting-state functional connectivity (RSFC) patterns in MDD using weighted global brain connectivity (wGBC) method.

Investigation into local white matter abnormality in emotional processing and sensorimotor areas using an automatically annotated fiber clustering in major depressive disorder.

This work presents an automatically annotated fiber cluster (AAFC) method to enable identification of anatomically meaningful white matter structures from the whole brain tractography. The proposed method consists of 1) a study-specific whole brain white matter parcellation using a well-established data-driven groupwise fiber clustering pipeline to segment tractography into multiple fiber clusters, and 2) a novel cluster annotation method to automatically assign an anatomical tract annotation to each fiber cluster by employing cortical parcellation information across multiple subjects. The novelty of the AAFC method is that it leverages group-wise information about the fiber clusters, including their fiber geometry and cortical terminations, to compute a tract anatomical label for each cluster in an automated fashion.