As raw sensory data are partial, our visual system extensively fills in missing details, creating enriched percepts based on incomplete bottom-up information. Despite evidence for internally generated representations at early stages of cortical processing, it is not known whether these representations include missing information of dynamically transforming objects. Long-range apparent motion (AM) provides a unique test case because objects in AM can undergo changes both in position and in features. Using fMRI and encoding methods, we found that the "intermediate" orientation of an apparently rotating grating, never presented in the retinal input but interpolated during AM, is reconstructed in population-level, feature-selective tuning responses in the region of early visual cortex (V1) that corresponds to the retinotopic location of the AM path. This neural representation is absent when AM inducers are presented simultaneously and when AM is visually imagined. Our results demonstrate dynamic filling-in in V1 for object features that are interpolated during kinetic transformations.C ontrary to our seamless and unobstructed perception of visual objects, raw sensory data are often partial and impoverished. Thus, our visual system regularly fills in extensive details to create enriched representations of visual objects (1, 2). A growing body of evidence suggests that "filled-in" visual features of an object are represented at early stages of cortical processing where physical input is nonexistent. For example, increased activity in early visual cortex (V1) was found in retinotopic locations corresponding to nonstimulated regions of the visual field during the perception of illusory contours (3, 4) and color filling-in (5). Furthermore, recent functional magnetic resonance imaging (fMRI) studies using multivoxel pattern analysis (MVPA) methods show how regions of V1 lacking stimulus input can contain information regarding objects or scenes presented at other locations in the visual field (6, 7), held in visual working memory (8, 9), or used in mental imagery (10-13).Although these studies have found evidence for internally generated representations of static stimuli in early cortical processing, the critical question remains of whether and how interpolated visual feature representations are reconstructed in early cortical processing while objects undergo kinetic transformations, a situation that is more prevalent in our day-to-day perception.To address this question, we examined the phenomenon of long-range apparent motion (AM): when a static stimulus appears at two different locations in succession, a smooth transition of the stimulus across the two locations is perceived (14-16). Previous behavioral studies have shown that subjects perceive illusory representations along the AM trajectory (14,17) and that these representations can interfere with the perception of physically presented stimuli on the AM path (18-21). In line with this behavioral evidence, it was found that the perception of AM leads to increased bloo...

The current era of advanced computing has allowed for the development and implementation of the field of radiomics. In pediatric neuro-oncology, radiomics has been applied in determination of tumor histology, identification of disseminated disease, prognostication, and molecular classification of tumors (i.e., radiogenomics). The field also comes with many challenges, such as limitations in study sample sizes, class imbalance, generalizability of the methods, and data harmonization across imaging centers. The aim of this review paper is two-fold: first, to summarize existing literature in radiomics of pediatric neuro-oncology; second, to distill the themes and challenges of the field and discuss future directions in both a clinical and technical context.

Background Brain tumors are the most common solid tumors and the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high interoperator variability. We present a multi-institutional deep learning-based method for automated brain extraction and segmentation of pediatric brain tumors based on multi-parametric MRI scans. Methods Multi-parametric scans (T1w, T1w-CE, T2, and T2-FLAIR) of 244 pediatric patients (n=215 internal and n=29 external cohorts) with de novo brain tumors, including a variety of tumor subtypes, were preprocessed and manually segmented to identify the brain tissue and tumor subregions into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). The internal cohort was split into training (n=151), validation (n=43), and withheld internal test (n=21) subsets. DeepMedic, a three-dimensional convolutional neural network, was trained and the model parameters were tuned. Finally, the network was evaluated on the withheld internal and external test cohorts. Results Dice similarity score (median±SD) was 0.91±0.10/0.88±0.16 for the whole tumor, 0.73±0.27/0.84±0.29 for ET, 0.79±19/0.74±0.27 for union of all non-enhancing components (i.e., NET, CC, ED), and 0.98±0.02 for brain tissue in both internal/external test sets. Conclusions Our proposed automated brain extraction and tumor subregion segmentation models demonstrated accurate performance on segmentation of the brain tissue and whole tumor regions in pediatric brain tumors and can facilitate detection of abnormal regions for further clinical measurements.

Theories of embodied cognition propose that we recognize tools in part by reactivating sensorimotor representations of tool use in a process of simulation. If motor simulations play a causal role in tool recognition then performing a concurrent motor task should differentially modulate recognition of experienced vs. non-experienced tools. We sought to test the hypothesis that an incompatible concurrent motor task modulates conceptual processing of learned vs. non-learned objects by directly manipulating the embodied experience of participants. We trained one group to use a set of novel, 3-D printed tools under the pretense that they were preparing for an archeological expedition to Mars (manipulation group); we trained a second group to report declarative information about how the tools are stored (storage group). With this design, familiarity and visual attention to different object parts was similar for both groups, though their qualitative interactions differed. After learning, participants made familiarity judgments of auditorily presented tool names while performing a concurrent motor task or simply sitting at rest. We showed that familiarity judgments were facilitated by motor state-dependence; specifically, in the manipulation group, familiarity was facilitated by a concurrent motor task, whereas in the spatial group familiarity was facilitated while sitting at rest. These results are the first to directly show that manipulation experience differentially modulates conceptual processing of familiar vs. unfamiliar objects, suggesting that embodied representations contribute to recognizing tools.

Increasing evidence suggests that besides mutational and molecular alterations, the immune component of the tumor microenvironment also substantially impacts tumor behavior and complicates treatment response, particularly to immunotherapies. Although the standard method for characterizing tumor immune profile is through performing integrated genomic analysis on tissue biopsies, the dynamic change in the immune composition of the tumor microenvironment makes this approach not feasible, especially for brain tumors. Radiomics is a rapidly growing field that uses advanced imaging techniques and computational algorithms to extract numerous quantitative features from medical images. Recent advances in machine learning methods are facilitating biological validation of radiomic signatures and allowing them to “mine” for a variety of significant correlates, including genetic, immunologic, and histologic data. Radiomics has the potential to be used as a non-invasive approach to predict the presence and density of immune cells within the microenvironment, as well as to assess the expression of immune-related genes and pathways. This information can be essential for patient stratification, informing treatment decisions and predicting patients’ response to immunotherapies. This is particularly important for tumors with difficult surgical access such as gliomas. In this review, we provide an overview of the glioma microenvironment, describe novel approaches for clustering patients based on their tumor immune profile, and discuss the latest progress on utilization of radiomics for immune profiling of glioma based on current literature.