The incidence of adolescent depressive disorder is globally skyrocketing in recent decades, albeit the causes and the decision deficits depression incurs has yet to be well-examined. With an instrumental learning task, the aim of the current study is to investigate the extent to which learning behavior deviates from that observed in healthy adolescent controls and track the underlying mechanistic channel for such a deviation.

We recruited a group of adolescents with major depression and age-matched healthy control subjects to carry out the learning task with either gain or loss outcome and applied a reinforcement learning model that dissociates valence (positive v. negative) of reward prediction error and selection (chosen v. unchosen).

The results demonstrated that adolescent depressive patients performed significantly less well than the control group. Learning rates suggested that the optimistic bias that overall characterizes healthy adolescent subjects was absent for the depressive adolescent patients. Moreover, depressed adolescents exhibited an increased pessimistic bias for the counterfactual outcome. Lastly, individual difference analysis suggested that these observed biases, which significantly deviated from that observed in normal controls, were linked with the severity of depressive symoptoms as measured by HAMD scores.

By leveraging an incentivized instrumental learning task with computational modeling within a reinforcement learning framework, the current study reveals a mechanistic decision-making deficit in adolescent depressive disorder. These findings, which have implications for the identification of behavioral markers in depression, could support the clinical evaluation, including both diagnosis and prognosis of this disorder.

The role of diffusion tensor tractography (DTT) has become increasingly important in the preoperative mapping of brain white matter. Recently, functional magnetic resonance imaging (fMRI) driven DTT has provided the ability to evaluate the spatial relationship between the corticospinal tract (CST) and motor resection tumor boundaries. The main objective of this study was improvement of the preoperative assessment of the CST in patients with gliomas involving the motor cortical areas.

Seventeen patients with gliomas involving motor cortical areas underwent 3 dimensions (3D) T1-weighted imaging for anatomical referencing, using both fMRI and diffusion tensor imaging (DTI). We used the fast-marching tractography (FMT) algorithm to define the 3D connectivity maps within the whole brain using seed points selected in the white matter adjacent to the location of fMRI activation. The target region of interest (ROI) was placed in the cerebral peduncle. Karnofsky performance status (KPS) scores were evaluated for each patient before and after surgery.

The CST of a total seventeen patients were successfully tracked by choosing seed and target ROI on the path of the fibers. What is more, DTT can indicate preoperatively the possibility for total glioma removal or the maximum extent of surgical resection. The postoperative average KPS score for the seventeen patients enrolled increased by more than 10 points.

Incorporation of fMRI driven DTT showed a maximum benefit in surgical treatment of gliomas. Our study of the assessment precision should enhance the accuracy of glioma operations with a resulting improvement in postoperative patient outcome.