Animacy and real-world size shape object representations in the human medial temporal lobes

Identifying what an object is, and whether an object has been encountered before, is a crucial aspect of human behavior. Despite this importance, we do not yet have a complete understanding of the neural basis of these abilities. Investigations into the neural organization of human object representations have revealed category specific organization in the ventral visual stream in perceptual tasks. Interestingly, these categories fall within broader domains of organization, with reported distinctions between animate, inanimate large, and inanimate small objects. While there is some evidence for category specific effects in the medial temporal lobe (MTL), in particular in perirhinal and parahippocampal cortex, it is currently unclear whether domain level organization is also present across these structures. To this end, we used fMRI with a continuous recognition memory task. Stimuli were images of objects from several different categories, which were either animate or inanimate, or large or small within the inanimate domain. We employed representational similarity analysis (RSA) to test the hypothesis that object-evoked responses in MTL structures during recognition-memory judgments also show evidence for domain-level organization along both dimensions. Our data support this hypothesis. Specifically, object representations were shaped by either animacy, real-world size, or both, in perirhinal and parahippocampal cortex, and the hippocampus. While sensitivity to these dimensions differed across structures when probed individually, hinting at interesting links to functional differentiation, similarities in organization across MTL structures were more prominent overall. These results argue for continuity in the organization of object representations in the ventral visual stream and the MTL

Late positive complex in event-related potentials tracks memory signals when they are decision relevant.

The Late Positive Complex (LPC) is an Event-Related Potential (ERP) consistently observed in recognition-memory paradigms. In the present study, we investigated whether the LPC tracks the strength of multiple types of memory signals, and whether it does so in a decision dependent manner. For this purpose, we employed judgements of cumulative lifetime exposure to object concepts, and judgements of cumulative recent exposure (i.e., frequency judgements) in a study-test paradigm. A comparison of ERP signatures in relation to degree of prior exposure across the two memory tasks and the study phase revealed that the LPC tracks both types of memory signals, but only when they are relevant to the decision at hand. Another ERP component previously implicated in recognition memory, the FN400, showed a distinct pattern of activity across conditions that differed from the LPC; it tracked only recent exposure in a decision-dependent manner. Another similar ERP component typically linked to conceptual processing in past work, the N400, was sensitive to degree of recent and lifetime exposure, but it did not track them in a decision dependent manner. Finally, source localization analyses pointed to a potential source of the LPC in left ventral lateral parietal cortex, which also showed the decision-dependent effect. The current findings highlight the role of decision making in ERP markers of prior exposure in tasks other than those typically used in studies of recognition memory, and provides an initial link between the LPC and the previously suggested role of ventral lateral parietal cortex in memory judgements

Combining prostate health index and mpMRI data (MRI Spectroscopy) to manage PI-RADS lesions and reduce excessive biopsy, a single center study

To evaluate the values of PHI and PI-RADS findings in the early detection and prediction of prostate cancer, as well as their application in clinical trials, especially when values of PSA are in the „ grey zone„ with negative DRE. The 100 patients, men aged 50 years or older with prostate-specific antigen 4 to 10 ng/ml („gray zone„) and normal digital rectal examination with suspected prostate cancer were examined, who had undergone biopsy and were divided in two groups. A group with no evidence of PCa (non PCa) and the group with PCa. The performance of PHI and mpMRI PI-RADS score was compared to predict biopsy results and, specifically, the presence of clinically significant prostate cancer (csPCa) using multiple criteria. Among 100 subjects, 21 (21.0%) were diagnosed with PC, including 13 (61.95%) with csPC (Gleason≥7). By the threshold of PHI≥36, the sensitivity, specificity, PPV, and NPV to predict PCa were 100%, 68.35%, 45.65%, and 100%, respectively. The best cut-off (PHI) was 42.8% with sensitivity 85.7% and specificity 86.1%. The area under the receiver operator characteristic curve (AUC) of combining PHI and mpMRI was greater than that of PHI alone (0.993 vs. 0.954, p=0.002) and mpMRI alone (0.993 vs. 0.976, p=0.025). Comparing the performance in the identification of clinically significant prostate cancer (csPCa), we found that PHI ≥ 73.04 and PI-RADS score ≥ 4 were able to identify csPCa (Gleason score ≥ 7 (3 + 4)) both alone and added to a base model including age, PSA, fPSA-to-tPSA ratio and prostate volume. If biopsy was restricted to patients with PI-RADS 5 as well as PI-RADS 3 or 4 and PHI≥36.0, 50% of biopsy could be avoided with one csPCa patient being missed. The analyzed correlation between PHI and PI-RADS score was statistically significant (p<0.0001). According to the value of Spearman's coefficient, R=0.748, the correlation is positive, i.e. direct, and they showed that with an increase in the value of the prostatic health index, (PHI) the PI-RADS score increases, and vice versa. The combination of PHI and mpMRI had higher accuracy for detection of csPC compared with PHI or mpMRI alone. Keywords: Prostate health index, mpMRI PI-RADS, detection of prostate cance

Video object recognition based on deep learning

In dieser Masterarbeit haben wir eine Client-Server-Architektur zur automatischen Erkennung von Reklametafeln in Videostreams entwickelt. Die Client-Seite wurde mit einer Android-Anwendung umgesetzt, die dem Zweck dient Videodaten für die Server-Seite zu sammeln. Für die Server-Seite hingegen wurde StefanNet entwickelt, wobei es sich um ein Deep Neural Network handelt. StefanNet kann Reklametafeln in einem Video Frame erkennen und klassifizieren. StefanNet hat einen Feature Extractor mit 23 konvolutionären Ebenen und benutzt einen Single Shot Detector (SSD) zur Objekterkennung. Das Netz wurde mit dem selbst erstellten BillboardDataset trainiert, welches 4042 Beispielbilder beinhaltet, die von Reklametafeln in den U-Bahn-Stationen Wiens gemacht wurden. Zusätzlich wurden Data-Augmentation-Techniken angewendet um den Datensatz künstlich um 25% zu vergrößern. Außerdem wurden Quantisierungstechniken auf StefanNet angewendet um die Bittiefe, die notwendig ist, um die Gewichte des Netzwerks zu speichern, von float32 auf float16 zu verringern. Wir haben die Performance von StefanNet evaluiert, indem wir es mit den state of the art Netzwerken ResNet, MobileNet, Inception und VGG16 verglichen haben. Der Validierungsdatensatz setzt sich zusammen aus Ansichten der Reklametafeln von vorne und von der Seite. StefanNet erreichte 91% mean average precision (mAp) auf dem Testdatensatz, 98% mAp für Ansichten von vorne und 82% mAp für Ansichten von der Seite. Die Inferenzgeschwindigkeit war 40 Bilder pro Sekunde (FPS) auf einer Nvidia 1080 Grafikkarte. Die quantisierte Version von StefanNet erreichte 91% mAp auf dem Testdatensatz, 96% mAp für Ansichten von vorne und 85% mAp für Ansichten von der Seite. Die Inferenzgeschwindigkeit für die quantisierte Version war 45 FPS. Sowohl StefanNet als auch dessen quantisierte Version hat eine höhere mAp als die anderen evaluierten Netzwerke erreicht. Das bestätigt, dass die Architektur von StefanNet die derzeit am Besten passende Architektur für das Problem der automatischen Reklametafel-Erkennung in einem Video Stream ist.In this master thesis we designed a client server system for automatic billboard recognition in video streams. The client side is represented by an Android application which serves the purpose of collecting various video data streams for the server side. For the server side a deep neural network, called StefanNet, was designed. StefanNet is a fully convolutional neural network which is able to properly classify and localize billboard objects within a video frame. StefanNet has a feature extractor which contains 23 convolutional layers and uses a single shot detector (SSD) as an object detector. StefanNet has been trained on the self-designed BillboardDataset which contains 4042 image samples taken from the billboards located throughout the metro stations in Vienna. Additionally, data augmentation techniques have been implemented to artificially augment the dataset with a 25% increase rate. Furthermore, the compression-based quantization technique has been applied to the StefanNet model to reduce the bit-width necessary for storing the weights of the network from float32 to float16. We evaluated the performance of StefanNet by comparing against the state-of-the-art networks ResNet, MobileNet, Inception and VGG16. The validation dataset contains both side and frontal views of the billboards. StefanNet achieved 91% mean average precision (mAp) on the test dataset, 98% mAp on the frontal view validation dataset and 82% mAp on the side view validation dataset. The inference rate was 40 FPS on a Nvidia 1080 graphics card. The quantized version of the StefanNet model achieved 91% mAp on the test dataset, 96% mAp on the frontal view validation dataset and 85% mAp on the side view validation at an inference rate of 45 FPS. In comparison to the other evaluated networks both the StefanNet model and the quantized version of the model produce superior results and outperform the benchmark network models on all datasets. This confirms that the architecture of StefanNet is currently the most suitable for the specific problem of automatic billboard detection in video streams.10

Case Report: Malignant Primary Sellar Paraganglioma With Unusual Genetic and Imaging Features.

Background: Paraganglioma occurs rarely in the sellar/parasellar region. Here, we report a patient with malignant paraganglioma with primary sellar location with unusual genetic and imaging features. Case Presentation: A 31-year-old male presented with mild hypertension, headache, nausea, and vomiting. A sellar/parasellar tumor mass was revealed by magnetic resonance imaging (MRI), while an endocrine work-up found partial hypopituitarism, suggesting that it was a non-functioning pituitary tumor. Antihypertensive therapy and hormone replacement were initiated. Tumor reduction was achieved with transsphenoidal neurosurgery. However, histological diagnosis was not possible due to extensive tissue necrosis. After 4 years of stable disease, the residual tumor showed re-growth requiring gamma knife radiosurgery. Four years after the radiosurgery, MRI showed a significant tumor progression leading to a second neurosurgery. This time, pathological and immunohistochemical findings revealed paraganglioma. Plasma levels of metanephrine and normetanephrine were normal. A gene sequencing panel performed on DNA extracted from blood excluded germline mutations in 17 susceptibility genes. The patient developed new tumor masses in the neck, and the third surgery was performed. Immunohistochemistry demonstrated lack of ATRX (alpha thalassemia/mental retardation syndrome X-linked) protein in tumor cells, indicating an ATRX gene mutation. Molecular genetic analysis performed on tumor DNA revealed a combination of ATRX and TP53 gene abnormalities; this was not previously reported in paraganglioma. MRI and 68Ga-DOTANOC PET/CT revealed the full extent of the disease. Therapy with somatostatin LAR and 177Lu-DOTATATE Peptide Receptor Radionuclide Therapy (PRRT) was initiated. Conclusion: Although rare, paraganglioma should be considered in the differential diagnosis of sellar/parasellar tumor lesions, even in the absence of typical imaging features. ATRX gene mutation in paraganglioma is an early predictor of malignant behavior and a potential novel therapeutic marker when pharmacological therapy targeting mutated ATRX becomes available

Plasma Amino Acids in NAFLD Patients with Obesity Are Associated with Steatosis and Fibrosis: Results from the MAST4HEALTH Study

Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) have been linked to changes in amino acid (AA) levels. The objective of the current study was to examine the relationship between MRI parameters that reflect inflammation and fibrosis and plasma AA concentrations in NAFLD patients. Plasma AA levels of 97 NAFLD patients from the MAST4HEALTH study were quantified with liquid chromatography. Medical, anthropometric and lifestyle characteristics were collected and biochemical parameters, as well as inflammatory and oxidative stress biomarkers, were measured. In total, subjects with a higher MRI-proton density fat fraction (MRI-PDFF) exhibited higher plasma AA levels compared to subjects with lower PDFF. The concentrations of BCAAs (p-Value: 0.03), AAAs (p-Value: 0.039), L-valine (p-Value: 0.029), L-tyrosine (p-Value: 0.039) and L-isoleucine (p-Value: 0.032) were found to be significantly higher in the higher PDFF group compared to lower group. Plasma AA levels varied according to MRI-PDFF. Significant associations were also demonstrated between AAs and MRI-PDFF and MRI-cT1, showing the potential utility of circulating AAs as diagnostic markers of NAFLD

Association of Dietary Patterns with MRI Markers of Hepatic Inflammation and Fibrosis in the MAST4HEALTH Study

Whereas the etiology of non-alcoholic fatty liver disease (NAFLD) is complex, the role of nutrition as a causing and preventive factor is not fully explored. The aim of this study is to associate dietary patterns with magnetic resonance imaging (MRI) parameters in a European population (Greece, Italy, and Serbia) affected by NAFLD. For the first time, iron-corrected T1 (cT1), proton density fat fraction (PDFF), and the liver inflammation fibrosis score (LIF) were examined in relation to diet. A total of 97 obese patients with NAFLD from the MAST4HEALTH study were included in the analysis. A validated semi-quantitative food frequency questionnaire (FFQ) was used to assess the quality of diet and food combinations. Other variables investigated include anthropometric measurements, total type 2 diabetes risk, physical activity level (PAL), and smoking status. Principal component analysis (PCA) was performed to identify dietary patterns. Six dietary patterns were identified, namely &ldquo;High-Sugar&rdquo;, &ldquo;Prudent&rdquo;, &ldquo;Western&rdquo;, &ldquo;High-Fat and Salt&rdquo;, &ldquo;Plant-Based&rdquo;, and &ldquo;Low-Fat Dairy and Poultry&rdquo;. The &ldquo;Western&rdquo; pattern was positively associated with cT1 in the unadjusted model (beta: 0.020, p-value: 0.025) and even after adjusting for age, sex, body mass index (BMI), PAL, smoking, the center of the study, and the other five dietary patterns (beta: 0.024, p-value: 0.020). On the contrary, compared with low-intake patients, those with medium intake of the &ldquo;Low-Fat Dairy and Poultry&rdquo; pattern were associated with lower values of cT1, PDFF, and LIF. However, patients with a &ldquo;Low-Fat Dairy and Poultry&rdquo; dietary pattern were negatively associated with MRI parameters (cT1: beta: &minus;0.052, p-value: 0.046, PDFF: beta: &minus;0.448, p-value: 0.030, LIF: beta: &minus;0.408, p-value: 0.025). Our findings indicate several associations between MRI parameters and dietary patterns in NAFLD patients, highlighting the importance of diet in NAFLD