Comparison between gradients and parcellations for functional connectivity prediction of behavior.

Resting-state functional connectivity (RSFC) is widely used to predict behavioral measures. To predict behavioral measures, representing RSFC with parcellations and gradients are the two most popular approaches. Here, we compare parcellation and gradient approaches for RSFC-based prediction of a broad range of behavioral measures in the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. Among the parcellation approaches, we consider group-average "hard" parcellations (Schaefer et al., 2018), individual-specific "hard" parcellations (Kong et al., 2021a), and an individual-specific "soft" parcellation (spatial independent component analysis with dual regression; Beckmann et al., 2009). For gradient approaches, we consider the well-known principal gradients (Margulies et al., 2016) and the local gradient approach that detects local RSFC changes (Laumann et al., 2015). Across two regression algorithms, individual-specific hard-parcellation performs the best in the HCP dataset, while the principal gradients, spatial independent component analysis and group-average "hard" parcellations exhibit similar performance. On the other hand, principal gradients and all parcellation approaches perform similarly in the ABCD dataset. Across both datasets, local gradients perform the worst. Finally, we find that the principal gradient approach requires at least 40 to 60 gradients to perform as well as parcellation approaches. While most principal gradient studies utilize a single gradient, our results suggest that incorporating higher order gradients can provide significant behaviorally relevant information. Future work will consider the inclusion of additional parcellation and gradient approaches for comparison.

Bridging therapies to liver transplantation for hepatocellular carcinoma: A bridge to nowhere?

BACKGROUNDS/AIMS: Liver Transplantation (LT) is a recognized treatment for Hepatocellular Carcinoma (HCC). The role of Bridging Therapies (BT) remains controversial. METHODS: From January 2001 to October 2012, 192 patients were referred to the National University Hospital, Singapore for consideration of LT for HCC. Sixty-five patients (33.8%) were found suitable for transplant and were placed on the waitlist. Analysis was performed in these patients. RESULTS: The most common etiology of HCC was Hepatitis B (n=28, 43.1%). Thirty-six patients (55.4%) received BT. Seventeen patients (47.2%) received TACE only, while 10 patients (27.8%) received radiofrequency ablation (RFA) only. The remaining patients received a combination of transarterial chemoembolization (TACE) and RFA. Baseline tumor and patient characteristics were comparable between the two groups. The overall dropout rate was 44.4% and 31.0% in the BT and non-BT groups, respectively (p=0.269). The dropout rate due to disease progression beyond criteria was 6.9% (n=2) in the non-bridged group and 22.2% (n=8) in the bridged group (p=0.089). Thirty-nine patients (60%) underwent LT, of which all patients who underwent Living Donor LT did not receive BT (n=4, 21.1%, p=0.030). The median time to LT was 180 days (range, 20-558 days) in the non-BT group and 291 days (range, 17-844 days) in the BT group (p=0.214). There was no difference in survival or recurrence between the BT and non-BT groups (p=0.862). CONCLUSIONS: BT does not influence the dropout rate or survival after LT but it should be considered in patients who are on the waitlist for more than 6 months.