Visual Abstract

Perception of an ambiguous apparent motion is influenced by the immediately preceding motion. In positive priming, when an observer is primed with a slow-pace (1–3 Hz) sequence of motion frames depicting unidirectional drift (e.g., Right–Right–Right–Right), subsequent sequences of ambiguous frames are often perceived to continue moving in the primed direction (illusory Right–Right …). Furthermore, priming an observer with a slow-pace sequence of rebounding apparent motion frames that alternate between opponently coded motion directions (e.g., Right–Left–Right–Left) leads to an illusory continuation of the two-step rebounding sequence in subsequent random frames. Here, we show that even more arbitrary two-step motion sequences can be primed; in particular, two-step motion sequences that alternate between non-opponently coded directions (e.g., Up–Right–Up–Right; staircase motion ) can be primed to be illusorily perceived in subsequent random frames. We found that staircase sequences, but not drifting or rebounding sequences, were primed more effectively with four priming frames compared with two priming frames, suggesting the importance of repeating the sequence element for priming arbitrary two-step motion sequences. Moreover, we compared the effectiveness of motion primes to that of symbolic primes (arrows) and found that motion primes were significantly more effective at producing prime-consistent responses. Although it has been proposed that excitatory and rivalry-like mechanisms account for drifting and rebounding motion priming, current motion processing models cannot account for our observed priming of staircase motion. We argue that higher order processes involving the recruitment and interaction of both attention and visual working memory are required to account for the type of two-step motion priming reported here.

No abstract

Background Whole brain segmentation from magnetic resonance imaging (MRI) remains a vital step in analysis workflows. We developed a deep learning framework that dramatically reduces the computational and human resources required for removal of non‐brain tissues. Segmentation of brain structures is currently resource intensive and remains a bottleneck in the analysis pipeline. Here we report on the capabilities of our previously developed deep learning framework to segment three specific brain structures of varying difficulty. Method We trained and tested a deep learning framework on three brain structures of varying difficulty: the Hippocampus, Cortical Grey Matter, and Lateral Ventricles. Training set size differed between segmentation task based on ground truth availability: Hippocampus (8,780 train, 878 test), Cortical Grey Matter (8,900 train, 890 test), Lateral Ventricle (8,700 train, 870 test). Data sets were derived from the following studies: ADNI, ADC, Khandle, Sol‐Inca and 90plus. At the core of our framework is a tunable multi‐stage convolutional neural network (CNN). The configuration used for these experiments is the same as our top performing configuration used for whole brain segmentation, a 5 stage 13 layer encoder followed by a 6 layer fusion decoder. All experiments were performed on an NVIDIA DGX Station with four Tesla V100 16GB GPUs. For both training and testing we used ground truth masks generated by atlas‐based segmentation techniques followed by human quality control. We used the F1 score, or dice similarity coefficient, between each prediction and ground truth mask to evaluate the quality. Result Sample predictions and their corresponding ground truth masks are shown in Figures 1‐3. F1 score distributions are shown in Figure 4 indicating performance in diverse imaging cohorts. General performance is high, but varies amongst ROIs. Outliers are most likely explained by low quality data. Conclusion Our preliminary results suggest our current deep learning framework can generalize relatively well without modification to its configuration, although performance for each ROI varies. Further modifications to the framework are needed to achieve production scale quality as well as increased quality control of ground truth masks to reduce the number of outliers and improve general performance.