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The processes underlying perceptual decision making are diverse and typically engage a distributed network of brain areas. It is a particular challenge to establish a sensory-to-motor functional hierarchy in such networks. This is because single-cell recordings mainly study the nodes of decision networks in isolation but seldom simultaneously. Moreover, imaging methods, which allow simultaneously accessing information from overall networks, typically suffer from either the temporal or the spatial resolution necessary to establish a detailed functional hierarchy in terms of a sequential recruitment of areas during a decision process. Here we report a novel analytical approach to work around these latter limitations: using temporal differences in human fMRI activation profiles during a tactile discrimination task with immediate versus experimentally delayed behavioral responses, we could derive a linear functional gradient across task-related brain areas in terms of their relative dependence on sensory input versus motor output. The gradient was established by comparing peak latencies of activation between the two response conditions. The resulting time differences described a continuum that ranged from zero time difference, indicative for areas that process information related to the sensory input and, thus, are invariant to the response delay instruction, to time differences corresponding to the delayed response onset, thus indicating motor-related processing. Taken together with our previous findings (Li Hegner et al. []: Hum Brain Mapp 36:3339-3350), our results suggest that the anterior insula reflects the ultimate perceptual stage within the uncovered sensory-to-motor gradient, likely translating sensory information into a categorical abstract (non-motor) decision. Hum Brain Mapp 38:1172-1181, 2017. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Perceptual decision making involves a distributed cortical network including areas related to sensory feature extraction, decision formation, and finally signalling the decision through a motor response. Although these processing steps are supposed to occur in sequence, the seemingly instant mapping of a perceptual decision onto a motor response renders these processes almost indistinguishable. To dissociate cortical areas related to sensory decision making from areas that prepare the subsequent motor response, we performed functional magnetic resonance imaging during a tactile spatial pattern discrimination task with interleaved immediate and delayed response conditions. Decision difficulty was manipulated parametrically by adding spatial noise to the tactile patterns, resulting in a rise in decision time with increasing noise. We assumed that areas involved in making the decision should show a variation in their activation with decision time and irrespective of whether (immediate response condition) or not (delayed response condition) a motor response could be prepared in advance. To exhibit these putative decision areas, we used response time, as was obtained in the immediate response condition, as parametric predictor for the difficulty-dependent variations of blood oxygenation level-dependent (BOLD)-activity in both response conditions. BOLD activations in right (contralateral) postcentral sulcus, right intraparietal sulcus (IPS) and bilateral anterior insula (aINS) reflected this parametric modulation in both response conditions, suggesting a role of these areas in tactile decisions independent of decision-specific motor preparation. Furthermore, a multivariate pattern analysis performed on the BOLD responses in the delayed response condition for a single difficulty level independently validated IPS and aINS as decision-related areas. Hum Brain Mapp 36:3339-3350, 2015. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Axions and axion‐like particles are hypothetical particles predicted in extensions of the standard model and are promising cold dark matter candidates. The Any Light Particle Search (ALPS II) experiment is a light‐shining‐through‐the‐wall experiment that aims to produce these particles from a strong light source and magnetic field and subsequently detect them through a reconversion into photons. With an expected rate ≈1 photon per day, a sensitive detection scheme needs to be employed and characterized. One foreseen detector is based on a transition edge sensor (TES). Here, the machine and deep learning algorithms for the rejection of background events recorded with the TES are investigated. A first application of convolutional neural networks to classify time series data measured with the TES is also presented. [ABSTRACT FROM AUTHOR]