In daily life, mobility requires walking while performing a cognitive or upper-extremity motor task. Although previous studies have evaluated the effects of dual tasks on gait performance, few studies have evaluated cortical activation and its association with gait disturbance during dual tasks. In this study, we simultaneously assessed gait performance and cerebral oxygenation in the bilateral prefrontal cortices (PFC), premotor cortices (PMC), and supplemental motor areas (SMA), using functional near-infrared spectroscopy, in 17 young adults performing dual tasks. Each participant was evaluated while performing normal-pace walking (NW), walking while performing a cognitive task (WCT), and walking while performing a motor task (WMT). Our results indicated that the left PFC exhibited the strongest and most sustained activation during WCT, and that NW and WMT were associated with minor increases in oxygenation levels during their initial phases. We observed increased activation in channels in the SMA and PMC during WCT and WMT. Gait data indicated that WCT and WMT both caused reductions in walking speed, but these reductions resulted from differing alterations in gait properties. WCT was associated with significant changes in cadence, stride time, and stride length, whereas WMT was associated with reductions in stride length only. During dual-task activities, increased activation of the PMC and SMA correlated with declines in gait performance, indicating a control mechanism for maintaining gait performance during dual tasks. Thus, the regulatory effects of cortical activation on gait behavior enable a second task to be performed while walking.

Background: This study surveyed the characteristics of white matter (WM) micro-integrity in patients who were diagnosed with major depressive disorder (MDD) without co-morbidities.Method: A total of 44 patients with MDD and 27 normal controls were enrolled in our study. Diffusion tensor imaging images of patients and controls were pre-processed and analysed to estimate differences in WM micro-integrity between patients and controls by performing comparisons of the values obtained from fractional anisotropy (FA). FA outputs of patients and controls were compared by a non-parametric permutation-based method with global brain volume, age and gender as covariates. In addition, the between-group differences of radial diffusivity (RD) and axial diffusivity (AD) were assessed to explain the alterations in FA values. Correlations between clinical variables (such as depression severity, anxiety severity, illness duration) and FA values were also estimated in each group and across both groups.Results: The patients with MDD had significantly lower FA values than the controls, for the left superior longitudinal fasciculus (SLF) and the right anterior thalamic radiation (ATR). The reductions in FA values occurred in combination with elevated RD values in the bilateral SLF and decreased AD values in the bilateral ATR. FA values were negatively correlated with depression severity in the SLF and with illness duration in the right SLF and ATR.Conclusions: MDD patients had significant alterations in the WM micro-integrity of the left SLF and the right ATR.

Artificial intelligence (AI) has been applied with considerable success in the fields of radiology, pathology, and neurosurgery. It is expected that AI will soon be used to optimize strategies for the clinical management of patients based on intensive imaging follow-up. Our objective in this study was to establish an algorithm by which to automate the volumetric measurement of vestibular schwannoma (VS) using a series of parametric MR images following radiosurgery. Based on a sample of 861 consecutive patients who underwent Gamma Knife radiosurgery (GKRS) between 1993 and 2008, the proposed end-to-end deep-learning scheme with automated pre-processing pipeline was applied to a series of 1290 MR examinations (T1W+C, and T2W parametric MR images). All of which were performed under consistent imaging acquisition protocols. The relative volume difference (RVD) between AI-based volumetric measurements and clinical measurements performed by expert radiologists were + 1.74%, − 0.31%, − 0.44%, − 0.19%, − 0.01%, and + 0.26% at each follow-up time point, regardless of the state of the tumor (progressed, pseudo-progressed, or regressed). This study outlines an approach to the evaluation of treatment responses via novel volumetric measurement algorithm, and can be used longitudinally following GKRS for VS. The proposed deep learning AI scheme is applicable to longitudinal follow-up assessments following a variety of therapeutic interventions.