The dorsal visual pathway represents object-centered spatial relations for object recognition

Although there is mounting evidence that input from the dorsal visual pathway is crucial for object processes in the ventral pathway, the specific functional contributions of dorsal cortex to these processes remains poorly understood. Here, we hypothesized that dorsal cortex computes the spatial relations among an object's parts — a processes crucial for forming global shape percepts — and transmits this information to the ventral pathway to support object categorization. Using multiple functional localizers, we discovered regions in the intraparietal sulcus (IPS) that were selectively involved in computing object-centered part relations. These regions exhibited task-dependent functional connectivity with ventral cortex, and were distinct from other dorsal regions, such as those representing allocentric relations, 3D shape, and tools. In a subsequent experiment, we found that the multivariate response of posterior IPS, defined on the basis of part-relations, could be used to decode object category at levels comparable to ventral object regions. Moreover, mediation and multivariate connectivity analyses further suggested that IPS may account for representations of part relations in the ventral pathway. Together, our results highlight specific contributions of the dorsal visual pathway to object recognition. We suggest that dorsal cortex is a crucial source of input to the ventral pathway and may support the ability to categorize objects on the basis of global shape.

Assignment Flow for Order-Constrained OCT Segmentation

At the present time optical coherence tomography (OCT) is among the most commonly used non-invasive imaging methods for the acquisition of large volumetric scans of human retinal tissues and vasculature. The substantial increase of accessible highly resolved 3D samples at the optic nerve head and the macula is directly linked to medical advancements in early detection of eye diseases. To resolve decisive information from extracted OCT volumes and to make it applicable for further diagnostic analysis, the exact measurement of retinal layer thicknesses serves as an essential task be done for each patient separately. However, manual examination of OCT scans is a demanding and time consuming task, which is typically made difficult by the presence of tissue-dependent speckle noise. Therefore, the elaboration of automated segmentation models has become an important task in the field of medical image processing. We propose a novel, purely data driven geometric approach to order-constrained 3D OCT retinal cell layer segmentation which takes as input data in any metric space and can be implemented using only simple, highly parallelizable operations. As opposed to many established retinal layer segmentation methods, we use only locally extracted features as input and do not employ any global shape prior. The physiological order of retinal cell layers and membranes is achieved through the introduction of a smoothed energy term. This is combined with additional regularization of local smoothness to yield highly accurate 3D segmentations. The approach thereby systematically avoid bias pertaining to global shape and is hence suited for the detection of anatomical changes of retinal tissue structure. To demonstrate its robustness, we compare two different choices of features on a data set of manually annotated 3D OCT volumes of healthy human retina. The quality of computed segmentations is compared to the state of the art in automatic retinal layer segmention as well as to manually annotated ground truth data in terms of mean absolute error and Dice similarity coefficient. Visualizations of segmented volumes are also provided.

Auroral zone over the last 3000 years

We investigated the global shape of the auroral zone over the last 3000 years using paleomagnetism CALS models. A similar method of apex latitude as proposed by Oguti (1993a; 1993b) was adopted to draw the auroral zone. The Oguti method is examined using 50-year data from ground-based magnetometers located at high latitudes, using IGRF models. The equatorward auroral limit during magnetic storms was also examined using more than 20 years data from the DMSP satellites. The reconstructed auroral zone and the equatorward auroral limit were compared with the historical auroral witness records for 1200 AD and 1800 AD. We concluded that the 12th and 18th centuries were excellent periods for Japan and the United Kingdom, respectively, to observe auroras over the last 3000 years.

Predict Alzheimer’s disease using hippocampus MRI data: a lightweight 3D deep convolutional network model with visual and global shape representations

Background Alzheimer’s disease (AD) is a progressive and irreversible brain disorder. Hippocampus is one of the involved regions and its atrophy is a widely used biomarker for AD diagnosis. We have recently developed DenseCNN, a lightweight 3D deep convolutional network model, for AD classification based on hippocampus magnetic resonance imaging (MRI) segments. In addition to the visual features of the hippocampus segments, the global shape representations of the hippocampus are also important for AD diagnosis. In this study, we propose DenseCNN2, a deep convolutional network model for AD classification by incorporating global shape representations along with hippocampus segmentations. Methods The data was obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and was T1-weighted structural MRI from initial screening or baseline, including ADNI 1,2/GO and 3. DenseCNN2 was trained and evaluated with 326 AD subjects and 607 CN hippocampus MRI using 5-fold cross-validation strategy. DenseCNN2 was compared with other state-of-the-art machine learning approaches for the task of AD classification. Results We showed that DenseCNN2 with combined visual and global shape features performed better than deep learning models with visual or global shape features alone. DenseCNN2 achieved an average accuracy of 0.925, sensitivity of 0.882, specificity of 0.949, and area under curve (AUC) of 0.978, which are better than or comparable to the state-of-the-art methods in AD classification. Data visualization analysis through 2D embedding of UMAP confirmed that global shape features improved class discrimination between AD and normal. Conclusion DenseCNN2, a lightweight 3D deep convolutional network model based on combined hippocampus segmentations and global shape features, achieved high performance and has potential as an efficient diagnostic tool for AD classification.

Unique heliophysics science opportunities along the Interstellar Probe journey up to 1000 AU from the Sun

<p>The Interstellar Probe is a space mission to discover physical interactions shaping globally the boundary of our Sun`s heliosphere and its dynamics and for the first time directly sample the properties of the local interstellar medium (LISM). Interstellar Probe will go through the boundary of the heliosphere to the LISM enabling for the first time to explore the boundary with a dedicated instrumentation, to take the image of the global heliosphere by looking back and explore in-situ the unknown LISM. The pragmatic concept study of such mission with a lifetime 50 years that can be implemented by 2030 was funded by NASA and has been led by the Johns Hopkins University Applied Physics Laboratory (APL). The study brought together a diverse community of more than 400 scientists and engineers spanning a wide range of science disciplines across the world.</p><p>Compelling science questions for the Interstellar Probe mission have been with us for many decades. Recent discoveries from a number of space missions exploring the heliosphere raised new questions strengthening the science case. The very shape of the heliosphere, a manifestation of complex global interactions between the solar wind and the LISM, remains the biggest mystery. Interpretations of imaging the heliosphere in energetic neutral atoms (ENAs) in different energy ranges on IBEX and Cassini/INCA from inside show contradictory pictures. Global physics-based models also do not agree on the global shape. Interstellar Probe on outbound trajectory will image the heliosphere from outside for the first time and will provide a unique determination of the global shape.</p><p>The LISM is a completely new area for exploration and discovery. We have a crude understanding of the LISM inferred from in-situ measurements inside the heliosphere of interstellar helium, pick-up-ions, ENAs, remote observations of solar backscattered Lyman-alpha emission and absorption line spectroscopy in the lines of sight of stars. We have no in-situ measurements of most LISM properties, e.g. ionization, plasma and neutral gas, magnetic field, composition, dust, and scales of possible inhomogeneities. Voyagers with limited capabilities have explored 30 AU beyond the heliosphere which appeared to be a region of significant heliospheric influence. The LISM properties are among the key unknowns to understand the Sun`s galactic neighborhood and how it shapes our heliosphere. Interstellar Probe will be the first NASA mission to discover the very nature of the LISM and shed light on whether the Sun enters a new region in the LISM in the near future.</p><p>In this presentation we give an overview of heliophysics science for the Interstellar Probe mission focusing on the critical science questions of the three objectives for the mission. We will discuss in more details a need for direct measurements in the LISM uniquely enabled by the Interstellar Probe.</p>

Phoebe’s differentiated interior from refined shape analysis

Context. Phoebe is an irregular satellite of Saturn, and its origin, from either between the orbits of the giant planets or the Kuiper Belt, is still uncertain. The extent of differentiation of its interior can potentially help inform its formation location because it is mainly determined by heat from 26-aluminum. The internal structure is reflected in the shape, assuming the body is relaxed to hydrostatic equilibrium. Although previous data analysis indicates Phoebe is close to hydrostatic equilibrium, its heavily cratered surface makes it difficult to tease out its low-order shape characteristics. Aims. This paper aims to extract Phoebe’s global shape from the observations returned by the Cassini mission for comparison with uniform and stratified interior models under the assumption of hydrostatic equilibrium. Methods. The global shape is derived from fitting spherical harmonics and keeping only the low-degree harmonics that represent the shape underneath the heavily cratered surface. The hydrostatic theoretical model for shape interpretation is based on the Clairaut equation developed to the third order (although the second order is sufficient in this case). Results. We show that Phoebe is differentiated with a mantle density between 1900 and 2400 kg m−3. The presence of a porous surface layer further restricts the fit with the observed shape. This result confirms the earlier suggestion that Phoebe accreted with sufficient 26-aluminium to drive at least partial differentiation, favoring an origin with C-type asteroids.