Aberrant Dynamic Functional Connectivity of Posterior Cingulate Cortex Subregions in Major Depressive Disorder With Suicidal Ideation

Accumulating evidence indicates the presence of structural and functional abnormalities of the posterior cingulate cortex (PCC) in patients with major depressive disorder (MDD) with suicidal ideation (SI). Nevertheless, the subregional-level dynamic functional connectivity (dFC) of the PCC has not been investigated in MDD with SI. We therefore sought to investigate the presence of aberrant dFC variability in PCC subregions in MDD patients with SI. We analyzed resting-state functional magnetic resonance imaging (fMRI) data from 31 unmedicated MDD patients with SI (SI group), 56 unmedicated MDD patients without SI (NSI group), and 48 matched healthy control (HC) subjects. The sliding-window method was applied to characterize the whole-brain dFC of each PCC subregion [the ventral PCC (vPCC) and dorsal PCC (dPCC)]. In addition, we evaluated associations between clinical variables and the aberrant dFC variability of those brain regions showing significant between-group differences. Compared with HCS, the SI and the NSI groups exhibited higher dFC variability between the left dPCC and left fusiform gyrus and between the right vPCC and left inferior frontal gyrus (IFG). The SI group showed higher dFC variability between the left vPCC and left IFG than the NSI group. Furthermore, the dFC variability between the left vPCC and left IFG was positively correlated with Scale for Suicidal Ideation (SSI) score in patients with MDD (i.e., the SI and NSI groups). Our results indicate that aberrant dFC variability between the vPCC and IFG might provide a neural-network explanation for SI and may provide a potential target for future therapeutic interventions in MDD patients with SI

Abnormal Degree Centrality Associated With Cognitive Dysfunctions in Early Bipolar Disorder

Delayed diagnosis of bipolar disorder (BD) is common. However, diagnostic validity may be enhanced using reliable neurobiological markers for BD. Degree centrality (DC) is one such potential marker that enables researchers to visualize neuronal network abnormalities in the early stages of some neuropsychiatric disorders. In the present study, we measured resting-state DC abnormalities and cognitive deficits in order to identify early neurobiological markers for BD. We recruited 23 patients with BD who had recently experienced manic episodes (duration of illness <2 years) and 46 matched healthy controls. Our findings indicated that patients with BD exhibited DC abnormalities in frontal areas, temporal areas, the right postcentral gyrus, and the posterior lobe of the cerebellum. Moreover, correlation analysis revealed that psychomotor speed indicators were associated with DC in the superior temporal and inferior temporal gyri, while attention indicators were associated with DC in the inferior temporal gyrus, in patients with early BD. Our findings suggest that DC abnormalities in neural emotion regulation circuits are present in patients with early BD, and that correlations between attention/psychomotor speed deficits and temporal DC abnormalities may represent early markers of BD

An Explanatory Analysis of Driver Injury Severity in Rear-End Crashes Using a Decision Table/Naïve Bayes (Dtnb) Hybrid Classifier

Rear-end crashes are a major type of traffic crashes in the U.S. Of practical necessity is a comprehensive examination of its mechanism that results in injuries and fatalities. Decision table (DT) and Naïve Bayes (NB) methods have both been used widely but separately for solving classification problems in multiple areas except for traffic safety research. Based on a two-year rear-end crash dataset, this paper applies a decision table/Naïve Bayes (DTNB) hybrid classifier to select the deterministic attributes and predict driver injury outcomes in rear-end crashes. The test results show that the hybrid classifier performs reasonably well, which was indicated by several performance evaluation measurements, such as accuracy, F-measure, ROC, and AUC. Fifteen significant attributes were found to be significant in predicting driver injury severities, including weather, lighting conditions, road geometry characteristics, driver behavior information, etc. The extracted decision rules demonstrate that heavy vehicle involvement, a comfortable traffic environment, inferior lighting conditions, two-lane rural roadways, vehicle disabled damage, and two-vehicle crashes would increase the likelihood of drivers sustaining fatal injuries. The research limitations on data size, data structure, and result presentation are also summarized. The applied methodology and estimation results provide insights for developing effective countermeasures to alleviate rear-end crash injury severities and improve traffic system safety performance