A5082870988- Advanced MRI Techniques and Applications
- Functional Brain Connectivity Studies
- Advanced Neuroimaging Techniques and Applications
- Atomic and Subatomic Physics Research
- Neural dynamics and brain function
- Medical Imaging Techniques and Applications
- Neural and Behavioral Psychology Studies
- Mental Health Research Topics
- Cell Image Analysis Techniques
- NMR spectroscopy and applications
- EEG and Brain-Computer Interfaces
- Decision-Making and Behavioral Economics
- Health, Environment, Cognitive Aging
- Advanced NMR Techniques and Applications
- Heart Rate Variability and Autonomic Control
- MRI in cancer diagnosis
- Advanced Data Processing Techniques
- Diversity and Career in Medicine
- Geological and Geophysical Studies
- Neuroscience and Music Perception
- Brain Tumor Detection and Classification
- Advanced Vision and Imaging
- Machine Learning in Materials Science
- Advanced Image Processing Techniques
- Astro and Planetary Science
University Health Network
2020-2024
Ontario Brain Institute
2023-2024
University of Zurich
2014-2023
ETH Zurich
2014-2023
Institute for Biomedical Engineering
2014-2023
Krembil Research Institute
2023
Health Net
2022
Laboratory for Social and Neural Systems Research
2009-2020
Zürcher Fachhochschule
2020
Max Planck Institute for Biophysical Chemistry
2009
Physiological noise is one of the major confounds for fMRI. A common class correction methods model from peripheral measures, such as ECGs or pneumatic belts. However, physiological has not emerged a standard preprocessing step fMRI data yet due to: (1) varying quality recordings, (2) non-standardized formats and (3) lack full automatization processing modeling physiology, required large-cohort studies.We introduce PhysIO Toolbox recordings model-based correction. It implements variety...
Inferring on others' (potentially time-varying) intentions is a fundamental problem during many social transactions. To investigate the underlying mechanisms, we applied computational modeling to behavioral data from an economic game in which 16 pairs of volunteers (randomly assigned “player” or “adviser” roles) interacted. The player performed probabilistic reinforcement learning task, receiving information about binary lottery visual pie chart. adviser, who received more predictive...
Abstract This work demonstrates a fast, sensitive method of characterizing the dynamic performance MR gradient systems. The accuracy time‐courses is often compromised by field imperfections various causes, including eddy currents and mechanical oscillations. Characterizing these perturbations instrumental for corrections pre‐emphasis or post hoc signal processing. Herein, chain treated as linear time‐invariant system, whose impulse response function determined measuring responses to known...
Social learning is fundamental to human interactions, yet its computational and physiological mechanisms are not well understood. One prominent open question concerns the role of neuromodulatory transmitters. We combined fMRI, modelling genetics address this in two separate samples (N = 35, N 47). Participants played a game requiring inference on an adviser’s intentions whose motivation help or mislead changed over time. Our analyses suggest that hierarchically structured belief updates...
The development of whole-brain models that can infer effective (directed) connection strengths from fMRI data represents a central challenge for computational neuroimaging. A recently introduced generative model data, regression dynamic causal modeling (rDCM), moves towards this goal as it scales gracefully to very large networks. However, large-scale networks with thousands connections are difficult interpret; additionally, one typically lacks information (data points per free parameter)...
Purpose MR image formation and interpretation relies on highly accurate dynamic magnetic fields of high fidelity. A range mechanisms still limit field fidelity, including magnet drifts, eddy currents, finite linearity stability power amplifiers used to drive gradient shim coils. Addressing remaining errors by means hardware, sequence, or signal processing optimizations, calls for immediate observation monitoring. The present work presents a stand‐alone monitoring system delivering insight...
Purpose Gradient imperfections remain a challenge in MRI, especially for sequences relying on long imaging readouts. This work aims to explore image reconstruction based k‐space trajectories predicted by an impulse response model of the gradient system. Theory and Methods characterization was performed twice with 3 years interval commercial Tesla (T) The measured functions were used predict actual single‐shot echo‐planar (EPI), spiral variable‐speed EPI sequences. Image phantom vivo data....
Purpose The purpose of this work was to improve the quality single‐shot spiral MRI and demonstrate its application for diffusion‐weighted imaging. Methods Image formation is based on an expanded encoding model that accounts dynamic magnetic fields up third order in space, nonuniform static B 0 , coil sensitivity encoding. determined by mapping, concurrent field monitoring. Reconstruction performed iterative inversion signal equations. Diffusion‐tensor imaging with readouts a phantom vivo,...
Diffusion MRI is compromised by unknown field perturbation during image encoding. The purpose of this study was to address problem using the recently described approach concurrent magnetic monitoring.Magnetic dynamics were monitored echo planar imaging readout a common diffusion-weighted sequence an integrated camera setup. encoding including changes over duration entire scans quantified and analyzed. Field perturbations corrected accounting for them in generalized reconstruction. impact on...
Navigating the physical world requires learning probabilistic associations between sensory events and their change in time (volatility). Bayesian accounts of this process rest on hierarchical prediction errors (PEs) that are weighted by estimates uncertainty (or its inverse, precision). In a previous fMRI study we found low-level precision-weighted PEs about visual outcomes (that update beliefs associations) activated putative dopaminergic midbrain; contrast, cue-outcome volatility)...
While persecutory delusions (PDs) have been linked to fallacies of reasoning and social inference, computational characterizations delusional tendencies are rare. Here, we examined 151 individuals from the general population on opposite ends PD spectrum (Paranoia Checklist [PCL]). Participants made trial-wise predictions in a probabilistic lottery, guided by advice more informed human nonsocial cue. Additionally, 2 frames differentially emphasized causes invalid advice: (a) adviser's...
Sinusoidal gradient oscillations in the kilohertz range are proposed for position tracking of NMR probes and prospective motion correction arbitrary imaging sequences without any alteration sequence timing. The method is combined with concurrent field monitoring to robustly perform image reconstruction presence potential dynamic deviations.Benchmarking experiments were done assess accuracy precision compare it theoretical predictions based on probe's time-dependent signal-to-noise ratio. An...
Spiral fMRI has been put forward as a viable alternative to rectilinear echo-planar imaging, in particular due its enhanced average k-space speed and thus high acquisition efficiency. This renders spirals attractive for contemporary applications that require spatiotemporal resolution, such laminar or columnar fMRI. However, practice, spiral is typically hampered by reduced robustness ensuing blurring artifacts, which arise from imperfections both static dynamic magnetic fields. Recently,...
Purpose To assess the utility of concurrent magnetic field monitoring for observing and correcting variations in k‐space trajectories global background fields that occur single‐shot echo planar imaging (EPI) time series as typically used functional MRI (fMRI). Methods Field was performed using an array NMR probes operated concurrently with EPI acquisitions from a static phantom. The observed fluctuations evolution were analyzed terms their temporal spatial behavior at level well...
Connectomics is essential for understanding large-scale brain networks but requires that individual connection estimates are neurobiologically interpretable. In particular, a principle of organization reciprocal connections between cortical areas functionally asymmetric. This challenge fMRI-based connectomics in humans where only undirected functional connectivity routinely available. By contrast, whole-brain effective (directed) computationally challenging, and emerging methods require...