Gatestherkelsen0769

Object formation is considered the aim of perceptual organization, but such a proposition has been neglected in empirical studies. In the current study, we investigated the role of object formation in configural superiority. Essentially, discrimination on bar orientations was enhanced by adding a right angle to each of the bars. Such facilitation is due to the emergent feature (EF) of closure formed by combining the bars with right angles. To study object formation, visual stimuli were generated by random dot stereograms to form objects or holes in 3D. Behaviorally, we found that the EF of closure facilitated oddball discrimination on objects, as demonstrated by previous studies, but did not facilitate oddball discrimination on holes with the same shape as objects. Multivariate pattern analysis of functional magnetic resonance imaging (fMRI) data showed that the EF of closure increased the object classification accuracy compared to the holes in the lateral occipital cortex (LOC), where object information is encoded, but not in the early visual cortex (EVC). The neural representations of objects and holes with and without EFs were further investigated using representational similarity analysis. The results demonstrate that in the LOC, the neural representations of objects with EFs showed a greater difference than those of the other three, that is, objects without EFs and holes with or without EFs. However, the uniqueness of objects with EFs was not observed in the EVC. Thus, our results suggest that the EF of closure, which leads to the configural superiority effect, only emerges for objects but not for holes, and only in the LOC but not the EVC. Our study provides the first empirical evidence suggesting that object formation plays an indispensable role in perceptual organization.

TMS neuronavigation with on-line display of the induced electric field (E-field) has the potential to improve quantitative targeting and dosing of stimulation, but present commercially available solutions are limited by simplified approximations.

Developing a near real-time method for accurate approximation of TMS induced E-fields with subject-specific high-resolution surface-based head models that can be utilized for TMS navigation.

Magnetic dipoles are placed on a closed surface enclosing an MRI-based head model of the subject to define a set of basis functions for the incident and total E-fields that define the subject's Magnetic Stimulation Profile (MSP). The near real-time speed is achieved by recognizing that the total E-field of the coil only depends on the incident E-field and the conductivity boundary geometry. The total E-field for any coil position can be obtained by matching the incident field of the stationary dipole basis set with the incident E-field of the moving coil and applying the same basis coefficients to the total E-field basis functions.

Comparison of the MSP-based approximation with an established TMS solver shows great agreement in the E-field amplitude (relative maximum error around 5%) and the spatial distribution patterns (correlation >98%). Computation of the E-field took ~100 ms on a cortical surface mesh with 120k facets.

The numerical accuracy and speed of the MSP approximation method make it well suited for a wide range of computational tasks including interactive planning, targeting, dosing, and visualization of the intracranial E-fields for near real-time guidance of coil positioning.

The numerical accuracy and speed of the MSP approximation method make it well suited for a wide range of computational tasks including interactive planning, targeting, dosing, and visualization of the intracranial E-fields for near real-time guidance of coil positioning.T2 quantification is commonly attempted by applying an exponential fit to proton density (PD) and transverse relaxation (T2)-weighted fast spin echo (FSE) images. However, inter-site studies have noted systematic differences between vendors in T2 maps computed via standard exponential fitting due to imperfect slice refocusing, different refocusing angles and transmit field (B1+) inhomogeneity. We examine T2 mapping at 3T across 13 sites and two vendors in healthy volunteers from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database using both a standard exponential and a Bloch modelling approach. The standard exponential approach resulted in highly variable T2 values across different sites and vendors. JAK inhibitor The two-echo fitting method based on Bloch equation modelling of the pulse sequence with prior knowledge of the nominal refocusing angles, slice profiles, and estimated B1+ maps yielded similar T2 values across sites and vendors by accounting for the effects of indirect and stimulated echoes. By modelling the actual refocusing angles used, T2 quantification from PD and T2-weighted images can be applied in studies across multiple sites and vendors.Drugs affecting neuromodulation, for example by dopamine or acetylcholine, take centre stage among therapeutic strategies in psychiatry. These neuromodulators can change both neuronal gain and synaptic plasticity and therefore affect electrophysiological measures. An important goal for clinical diagnostics is to exploit this effect in the reverse direction, i.e., to infer the status of specific neuromodulatory systems from electrophysiological measures. In this study, we provide proof-of-concept that the functional status of cholinergic (specifically muscarinic) receptors can be inferred from electrophysiological data using generative (dynamic causal) models. To this end, we used epidural EEG recordings over two auditory cortical regions during a mismatch negativity (MMN) paradigm in rats. All animals were treated, across sessions, with muscarinic receptor agonists and antagonists at different doses. Together with a placebo condition, this resulted in five levels of muscarinic receptor status. Using a dynamic that can serve as "computational assays" and infer the status of specific neuromodulatory systems in individual patients. This translational neuromodeling strategy has great promise for electrophysiological data in particular but requires careful validation. The present study demonstrates that the functional status of cholinergic (muscarinic) receptors can be inferred from electrophysiological data using dynamic causal models of neural circuits. While accuracy needs to be enhanced and our results must be replicated in larger samples, our current results provide proof-of-concept for computational assays of muscarinic function using EEG.