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Yayın The effect of the interaction between autistic traits and psychotic proneness on empathy: a cross-sectional study with a non-clinical sample(Emerald Publishing, 2023-11-08) Yıldırım, ElifPurpose: Recent evidence indicates an improving effect of the co-occurrence of autistic traits and psychotic symptoms on social cognition, but there is no agreement on the effect of the interaction between autistic traits and psychotic proneness on empathy. The aim of this study is to examine the effect of the interaction between autistic traits and positive psychotic experiences on cognitive and affective empathy. Design/methodology/approach: The sample consisted of 420 adults aged between 18 and 60. Assessments were administered anonymously online. Empathic abilities were evaluated by the Interpersonal Reactivity Index (IRI). While Autism Spectrum Quotient (AQ) was applied to measure autistic traits, The Community Assessment of Psychic Experience (CAPE) was used as a measurement of positive psychotic experiences. Findings: A series of regression analyses showed that although AQ and CAPE scores were not correlated with cognitive-IRI, the interaction between these scores predicted cognitive-IRI scores. It was found that the personal distress subscale of IRI was significantly associated with AQ, but this relationship was moderated by CAPE scores. Originality/value: These findings provide a different perspective on understanding social cognitive impairments in autism, which may have potential clinical implications. Findings also contribute to explaining the individual differences in empathic abilities.Yayın Automated cell nucleus detection for large-volume electron microscopy of neural tissue(IEEE, 2014-04-29) Tek, Faik Boray; Kroeger, Thorben; Hamprecht, Fred A.; Mikula, ShawnVolumetric electron microscopy techniques, such as serial block-face electron microscopy (SBEM), generate massive amounts of image data that are used for reconstructing neural circuits. Typically, this requires time-intensive manual annotation of cells and their connections. To facilitate this analysis, we study the problem of automated detection of cell nuclei in a new SBEM dataset that contains cerebral cortex, white matter, and striatum from an adult mouse brain. The dataset was manually annotated to identify the locations of all 3309 cell nuclei in the volume. We make both dataset and annotations available here. Using a hybrid approach that combines interactive learning, morphological processing, and object level feature classification, we demonstrate automated detection of cell nuclei at 92.4% recall and 95.1% precision. These algorithms are not RAM-limited and can scale to arbitrarily large datasets.Yayın Cross-sectional thermoacoustic imaging using multi-layer cylindrical media(IEEE, 2017-11-10) Elmas, Demet; Ünalmış Uzun, Banu; İdemen, Mehmet Mithat; Karaman, MustafaFor cross-sectional two-dimensional thermoacustic imaging of breast and brain, we explored solution of the wave equation using layered tissue model consisting of concentric annular layers on a cylindrical cross-section. To obtain the forward and inverse solutions of the thermoacoustic wave equation, we derived the Green's function involving Bessel and Hankel functions by employing the geometrical and acoustic parameters (densities and velocities) of layered media together with temporal initial condition, radiation conditions and continuity conditions on the layers' boundaries. The image reconstruction based on this approach involves the layer parameters as the apriori information which can be estimated from the acquired thermoacoustic data. To test and compare our layered solution with conventional solution based on homogeneous medium assumption, we performed simulations using numerical test phantoms consisting of sources distributed in the layered structure.Yayın Boundary element method for EEG single-dipole localization: a study in patients with OCD(Institute of Electrical and Electronics Engineers Inc., 2025-08-15) Abdullahi, Fatima I.; Demirer, Rüştü MuratThis study investigates EEG dipole localization in patients diagnosed with obsessive-compulsive disorder (OCD) using the Boundary Element Method (BEM) implemented via Brainstorm and OpenMEEG. EEG signals from 33 OCD patients were analyzed using a realistic, multi-layer head model consisting of scalp, skull, and brain tissues with respective conductivity values. Dipoles were accurately localized for each discrete time instant within the gamma frequency range (20-50 Hz) using a single dipole assumption per time point. EEG potentials measured from 19 standard electrodes were numerically computed by solving the forward EEG problem with the boundary element approach provided by OpenMEEG. Spectral clustering analysis identified distinct neural patterns corresponding to clinically recognized OCD subtypes, facilitating better diagnostic interpretations. Our results address previous methodological limitations by combining realistic head geometry modeling and precise temporal and spatial dipole estimation, offering promising directions for enhanced EEG-based diagnostic tools in psychiatry.
