Functional Connectivity in Gait Under Dual-Task Paradigm in Healthy Adolescents

PURPOSE: Functional connectivity can be viewed as the mechanism used to coordinate different neural networks in order to perform a complex task. Dual-task walking requires an individual to walk, while simultaneously performing a secondary task. The purpose of this study was to determine the level of functional connectivity and neuro-efficiency in adolescents under the dual-task walking. We hypothesized that we would see an increase in local and global efficiency within adolescents when transitioning from a single task gait test to a dual task gait test. METHODS: 15 healthy adolescents (12 male, age: 16.33±0.94 years, height: 1.69±0.10 m, mass: 64.08±9.81 kg) were recruited. The brain activity of the left and right prefrontal cortex (dorsal lateral, and dorsal media) were measured by fNIRS, the sampling rate of 20.3 Hz. Vicon motion capture system was used to record kinematic data, the sampling rate of 100 Hz. The first test was a single task gait test in which the subject walked at a self-selected speed between two cones 15 meters apart for 2 minutes with 10 seconds of standing as the baseline for fNIRS measures. Subjects were then tested under a dual-task paradigm (serially subtracting 7’s from randomly presented 2 or 3-digit numbers). The primary outcome measures include normalized local and global efficiency, gait speed, and stride length. Two two-way MANOVA with repeated measures were used to examine the task difference (alpha level = 0.05). RESULTS: There was a significant task effect on gait performance (F3,12 = 6.430, p = 0.008). Post hoc pairwise tests indicated that single-task presented greater average walking velocity (p \u3c 0.001, ST vs. DT: 1.33 ± 0.18 vs. 1.23 ± 0.20 m/s) and shorter stride time (p = 0.002, ST vs. DT: 1.11 ± 0.10 vs. 1.14 ± 0.12 s) than dual-task. There was no significant task effect on brain activity and neural efficiency (p \u3e 0.05). CONCLUSION: There was a significant difference in gait speed between adolescents and young adults. This is due to the task complexity affecting adolescents significantly more than adults. Young adults don’t see a change in speed but do see an increase in PFC activation. Adolescents having lower levels of functional connectivity compared to young adults could be due to the number/size of functionally connected regions measured within adolescence. Children are still developing day by day, indicating that the strength of functional connectivity seemingly develops as they age. With this information we can conclude that functional connectivity continuously changes while going through your adolescent years

An Element-wise RSAV Algorithm for Unconstrained Optimization Problems

We present a novel optimization algorithm, element-wise relaxed scalar auxiliary variable (E-RSAV), that satisfies an unconditional energy dissipation law and exhibits improved alignment between the modified and the original energy. Our algorithm features rigorous proofs of linear convergence in the convex setting. Furthermore, we present a simple accelerated algorithm that improves the linear convergence rate to super-linear in the univariate case. We also propose an adaptive version of E-RSAV with Steffensen step size. We validate the robustness and fast convergence of our algorithm through ample numerical experiments.Comment: 25 pages, 7 figure

Beam Retrieval: General End-to-End Retrieval for Multi-Hop Question Answering

Multi-hop QA involves finding multiple relevant passages and step-by-step reasoning to answer complex questions. While previous approaches have developed retrieval modules for selecting relevant passages, they face challenges in scenarios beyond two hops, owing to the limited performance of one-step methods and the failure of two-step methods when selecting irrelevant passages in earlier stages. In this work, we introduce Beam Retrieval, a general end-to-end retrieval framework for multi-hop QA. This approach maintains multiple partial hypotheses of relevant passages at each step, expanding the search space and reducing the risk of missing relevant passages. Moreover, Beam Retrieval jointly optimizes an encoder and two classification heads by minimizing the combined loss across all hops. To establish a complete QA system, we incorporate a supervised reader or a zero-shot GPT-3.5. Experimental results demonstrate that Beam Retrieval achieves a nearly 50% improvement compared with baselines on challenging MuSiQue-Ans, and it also surpasses all previous retrievers on HotpotQA and 2WikiMultiHopQA. Providing high-quality context, Beam Retrieval helps our supervised reader achieve new state-of-the-art performance and substantially improves (up to 28.8 points) the QA performance of zero-shot GPT-3.5.Comment: Code is available at https://github.com/canghongjian/beam_retrieve