Structural abnormalities in cortical volume, thickness, and surface area in 22q11.2 microdeletion syndrome: Relationship with psychotic symptoms.

Introduction22q11.2 deletion syndrome (22q11DS) represents one of the largest known genetic risk factors for psychosis, yet the neurobiological mechanisms underlying symptom development are not well understood. Here we conducted a cross-sectional study of 22q11DS to decompose cortical volume into its constituent parts, cortical thickness (CT) and surface area (SA), which are believed to have distinct neurodevelopmental origins.MethodsHigh-resolution T1-weighted scans were collected on 65 participants (31 22q11DS, 34 demographically comparable typically developing controls, 10-25 years old). Measures of cortical volume, CT, and SA were extracted from regions of interest using the FreeSurfer image analysis suite. Group differences and age-related trajectories in these structures, as well as their association with psychotic symptomatology, were assessed.ResultsRelative to controls, 22q11DS participants showed bilateral volumetric reductions in the inferior temporal cortex, fusiform gyrus, anterior cingulate, superior parietal cortex, and cuneus, which were driven by decreased SA in these regions. 22q11DS participants also had increased volumes, driven by increased CT, in bilateral insula regions. 22q11DS youth had increased CT in frontal regions, particularly middle frontal and medial orbitofrontal cortices. A pattern of age-associated cortical thinning was observed in typically developing controls in brain regions associated with visual and sensory information-processing (i.e., left pericalcarine cortex and fusiform gyrus, right lingual and postcentral cortices). However, this relationship was disrupted in 22q11DS participants. Finally, correlational analyses revealed that increased CT in right medial orbitofrontal cortex was associated with increased positive symptom severity in 22q11DS.ConclusionDifferential disruptions of CT and SA in distinct cortical regions in 22q11DS may indicate abnormalities in distinct developmental neural processes. Further, neuroanatomic abnormalities in medial frontal brain structures disproportionately affected in idiopathic schizophrenia were associated with psychotic symptom severity in 22q11DS youth, suggesting that disrupted biological processes in these cortical regions may underlie development of psychotic symptoms, both in 22q11DS and in the broader population

Losing oneself: the enigmatic other and the will to know in François Ozon's cinema

The formation of the self and the relationship between the psychosexual subject and normative power-relations have long been a contested field in both feminist and queer theory. Losing Oneself argues for a transitory model of the gendered psychosexual self; a model of the self as the effect of incorporating a separation and as a porous and negotiated entity. Traditional psychoanalysis, from Sigmund Freud to Jacques Lacan, has focused on loss and separation in the formation of the self. The self is subjugated to a law that both restricts and delimits that self as an imagined, complete figure. In two influential readings of this loss or incorporation of lack, Luce Irigaray and Judith Butler have both formulated melancholic models of the formation of the self. Driven by very different questions, these formulations attempt to explain the effects on the self by restrictive norms as well as explaining how the self enters the world as already a part and effect of that world. This thesis argues for a masochistic rather than melancholic incorporation of norms, emphasising the erotics and the pleasure as well as the pain and restriction at the centre of incorporation. At the core of this argumentation are challenges of the dualism of active/passive engagements with the world and the possibility to know oneself. Ozon’s cinema offers a unique challenge to the distinction between active and passive spectatorship. The films read in this thesis acts as active participants in the development of masochism as a model for affective response to an enigmatic world and its obfuscated demands on the self. Through readings of these films, the present thesis develops a model of the self that is simultaneously founded on lack, exclusion and subjugation but that also offers a pleasurable escape from subjugation and exclusion

Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room

Summary Objective Patients with suspicion of acute coronary syndrome (ACS) are difficult to diagnose and they represent a very heterogeneous group. Some require immediate treatment while others, with only minor disorders, may be sent home. Detecting ACS patients using a machine learning approach would be advantageous in many situations. Methods and materials Artificial neural network (ANN) ensembles and logistic regression models were trained on data from 634 patients presenting an emergency department with chest pain. Only data immediately available at patient presentation were used, including electrocardiogram (ECG) data. The models were analyzed using receiver operating characteristics (ROC) curve analysis, calibration assessments, inter- and intra-method variations. Effective odds ratios for the ANN ensembles were compared with the odds ratios obtained from the logistic model. Results The ANN ensemble approach together with ECG data preprocessed using principal component analysis resulted in an area under the ROC curve of 80%. At the sensitivity of 95% the specificity was 41%, corresponding to a negative predictive value of 97%, given the ACS prevalence of 21%. Adding clinical data available at presentation did not improve the ANN ensemble performance. Using the area under the ROC curve and model calibration as measures of performance we found an advantage using the ANN ensemble models compared to the logistic regression models. Conclusion Clinically, a prediction model of the present type, combined with the judgment of trained emergency department personnel, could be useful for the early discharge of chest pain patients in populations with a low prevalence of ACS

Explaining artificial neural network ensembles: A case study with electrocardiograms from chest pain patients

Artificial neural networks is one of the most commonly used machine learning algorithms in medical applications. However, they are still not used in practice in the clinics partly due to their lack of explanatory capacity. We compare two case-based explanation methods to two trained physicians on analysis of electrocardiogram (ECG) data from patients with a suspected acute coronary syndrome (ACS). The median overlaps of the top 5 selected features between the two physicians, and a given physician and a method, were initially low. Using a correlation analysis of the features the median overlap increased to values typically in the range 3-4. In conclusion, both our case-based methods generate explanations similar to those of trained expert physicians on the problem of diagnosing ACS from ECG data

Flow Matching for Scalable Simulation-Based Inference

Neural posterior estimation methods based on discrete normalizing flows have become established tools for simulation-based inference (SBI), but scaling them to high-dimensional problems can be challenging. Building on recent advances in generative modeling, we here present flow matching posterior estimation (FMPE), a technique for SBI using continuous normalizing flows. Like diffusion models, and in contrast to discrete flows, flow matching allows for unconstrained architectures, providing enhanced flexibility for complex data modalities. Flow matching, therefore, enables exact density evaluation, fast training, and seamless scalability to large architectures--making it ideal for SBI. We show that FMPE achieves competitive performance on an established SBI benchmark, and then demonstrate its improved scalability on a challenging scientific problem: for gravitational-wave inference, FMPE outperforms methods based on comparable discrete flows, reducing training time by 30% with substantially improved accuracy. Our work underscores the potential of FMPE to enhance performance in challenging inference scenarios, thereby paving the way for more advanced applications to scientific problems

Effektten av sponsoraktiviteter på eventarenaen

På bakgrunn av denne oppgaven ønsker vi å se på hvilken effekt sponsoratet har på eventarenaen. Vi benytter X Games Hafjell 2017 som utgangspunkt for analysen av tre utvalgte sponsorer; DnB, Renault og Mountain Dew. X Games gjennomførte i 2016 sitt aller første event på norsk jord, det fant sted i Oslo, og kan vise til et positivt driftsresultat. En effektanalyse målt av Menon Economics vises til at arrangementet har en høy tilfredshet og skaper positive assosiasjoner knyttet til Oslo som destinasjon (Kildal 2016). Dette førte oss til å undersøke i hvilken grad dette utgangspunktet ble ført videre til gjennomføringen i 2017 som fant sted på skidestinasjonen Hafjell. Dermed ble målet å undersøke i hvilken grad holdninger, kjennskap og samsvar mellom arrangør og sponsor skjer på den nye destinasjonen. På bakgrunn av dette førte det oss til problemstillingen: Hvilken effekt har sponsorater på eventarenaen under X Games? For å svare på denne problemstillingen valgte vi en kvantitativ tilnærming ved hjelp av spørreundersøkelser. Vi har gjennomført to undersøkelser med totalt 203 respondenter ved inngang og utgang på eventarenaen. Det er utformet seks analysespørsmål knyttet til problemstillingen, disse skal undersøke nærmere variablene holdning til sponsor og event, kjennskap, sosiale medier og samsvar. I undersøkelsen er det benyttet faktoranalyse for oppgavens begrepsvaliditet, reliabilitetstest for å undersøke konsistensen til utvalget. Analysespørsmålene har blitt utforsket ved hjelp av frekvenstabeller, regresjonsanalyser, korrelasjonsanalyser og t-test. Resultater fra undersøkelsen viser at det er en økt kjennskap til sponsorater, nedgang i holdninger til event og en forklaringskraft knyttet til samsvar opp mot kjøpsintensjon og holdning til sponsor