A5016083472- Neural dynamics and brain function
- Functional Brain Connectivity Studies
- Neuroscience and Neuropharmacology Research
- Medical Image Segmentation Techniques
- Advanced Neuroimaging Techniques and Applications
- Advanced MRI Techniques and Applications
- Surgical Simulation and Training
- Medical Imaging Techniques and Applications
- Augmented Reality Applications
- Domain Adaptation and Few-Shot Learning
- Brain Tumor Detection and Classification
- MRI in cancer diagnosis
- Cell Image Analysis Techniques
- Tryptophan and brain disorders
- Retinal Imaging and Analysis
- Advanced X-ray and CT Imaging
- Memory and Neural Mechanisms
- Research Data Management Practices
- Sulfur Compounds in Biology
- Robotics and Sensor-Based Localization
- Historical Geography and Cartography
- Image and Object Detection Techniques
- Advanced Radiotherapy Techniques
- Advanced Vision and Imaging
- Vascular Malformations Diagnosis and Treatment
Roche (Switzerland)
2020-2025
École Polytechnique Fédérale de Lausanne
2017-2024
Riga Technical University
2024
Newcastle University
2024
UNICEF East Asia and Pacific Regional Office
2018
Continental (Germany)
2018
Institute for Biomedicine
2018
University of Bern
2010-2016
Philips (Finland)
2014
Oxidative stress and glutathione (GSH) metabolism dysregulation has been implicated in the pathophysiology of schizophrenia. GAG-trinucleotide repeat (TNR) polymorphisms glutamate-cysteine ligase catalytic gene (GCLC), rate-limiting enzyme for GSH synthesis, are associated with In addition, may serve as a reserve pool neuronal glutamate (Glu) through γ-glutamyl cycle. The aim this study is to investigate brain [GSH] its association GCLC polymorphism, peripheral redox indices Glu.Magnetic...
Proton T 1 relaxation times of metabolites in the human brain have not previously been published at 7 T. In this study, values CH 3 and 2 group N ‐acetylaspartate total creatine as well nine other were measured occipital white matter gray using an inversion‐recovery technique combined with a newly implemented semi‐adiabatic spin‐echo full‐intensity acquired localized spectroscopy sequence (echo time = 12 ms). The mean ranged from 0.9 to 2.2 s. Among them, glutathione, scyllo‐inositol,...
Abstract Digital brain atlases define a hierarchy of regions and their locations in three-dimensional Cartesian space, providing standard coordinate system which diverse datasets can be integrated for visualization analysis. Although this has well-defined anatomical axes, it does not provide the best description complex geometries layered such as neocortex. As better alternative, we propose laminar systems that consider curvature structure region interest. These consist principal axis...
The function of the neocortex is fundamentally determined by its repeating microcircuit motif, but also rich, interregional connectivity. We present a data-driven computational model anatomy non-barrel primary somatosensory cortex juvenile rat, integrating whole-brain scale data while providing cellular and subcellular specificity. consists 4.2 million morphologically detailed neurons, placed in digital brain atlas. They are connected 14.2 billion synapses, comprising local, mid-range...
The CA1 region of the hippocampus is one most studied regions rodent brain, thought to play an important role in cognitive functions such as memory and spatial navigation. Despite a wealth experimental data on its structure function, it has been challenging integrate information obtained from diverse approaches. To address this challenge, we present community-based, full-scale silico model rat that integrates broad range data, synapse network, including reconstruction principal afferents,...
The function of the neocortex is fundamentally determined by its repeating microcircuit motif, but also rich, interregional connectivity. We present a data-driven computational model anatomy non-barrel primary somatosensory cortex juvenile rat, integrating whole-brain scale data while providing cellular and subcellular specificity. consists 4.2 million morphologically detailed neurons, placed in digital brain atlas. They are connected 14.2 billion synapses, comprising local, mid-range...
In connectomics, the study of network structure connected neurons, great advances are being made on two different scales: that macro- and meso-scale studying connectivity between populations micro-scale individual neurons. We combine these complementary views connectomics to build a first draft statistical model micro-connectome whole mouse neocortex based available data region-to-region whole-brain axon reconstructions. This process reveals targeting principle allows us predict innervation...
Abstract The CA1 region of the hippocampus is one most studied regions rodent brain, thought to play an important role in cognitive functions such as memory and spatial navigation. Despite a wealth experimental data on its structure function, it has been challenging reconcile information obtained from diverse approaches. To address this challenge, we present community-driven, full-scale silico model rat that integrates broad range data, synapse network, including reconstruction principal...
In this paper we propose a variational approach for multimodal image registration based on the diffeomorphic demons algorithm. Diffeomorphic has proven to be robust and efficient way intensity-based registration. However, main drawback is that it cannot deal with multiple modalities. We replace standard similarity metric (image intensity differences) by point-wise mutual information (PMI) in energy function. By comparing accuracy between our PMI B-Spline free-form deformation (FFD) simulated...
The function of the neocortex is fundamentally determined by its repeating microcircuit motif, but also rich, interregional connectivity. We present a data-driven computational model anatomy non-barrel primary somatosensory cortex juvenile rat, integrating whole-brain scale data while providing cellular and subcellular specificity. consists 4.2 million morphologically detailed neurons, placed in digital brain atlas. They are connected 14.2 billion synapses, comprising local, mid-range...
Background To investigate if non-rigid image-registration reduces motion artifacts in triggered and non-triggered diffusion tensor imaging (DTI) of native kidneys. A secondary aim was to determine, improvements through registration allow for omitting respiratory-triggering. Methods Twenty volunteers underwent coronal DTI the kidneys with nine b-values (10–700 s/mm2) at 3 Tesla. Image-registration performed using a multimodal nonrigid algorithm. Data processing yielded apparent coefficient...
Purpose To investigate if image registration of diffusion tensor imaging (DTI) allows omitting respiratory triggering for both transplanted and native kidneys Materials Methods Nine kidney transplant recipients eight healthy volunteers underwent renal DTI on a 3T scanner with without triggering. images were registered using multimodal nonrigid algorithm. Apparent coefficient (ADC), the contribution perfusion (F P ), fractional anisotropy (FA) determined. Relative root mean square errors...
Background. Patient-to-image registration is a core process of image-guided surgery (IGS) systems. We present novel approach for application in laparoscopic liver surgery, which reconstructs real time an intraoperative volume the underlying intrahepatic vessels through ultrasound (US) sweep process. Methods. An existing IGS system open procedure was adapted, with suitable instrument tracking equipment. Registration accuracy evaluated on realistic phantom by computing target error (TRE) 5...
Abstract Digital brain atlases define a hierarchy of regions and their locations in three-dimensional Cartesian space. They provide standard coordinate system which diverse datasets can be integrated for visualization analysis. Although this has well-defined anatomical axes, it does not the best context to work with complex geometries layered such as neocortex. To address that, we introduce laminar systems that consider curvature structure region interest. These new consist principal axis,...
In this paper we present a novel hybrid approach for multimodal medical image registration based on diffeomorphic demons. Diffeomorphic demons have proven to be robust and efficient way intensity-based registration. A very recent extension even allows use mutual information (MI) as similarity measure images. However, due the intensity correspondence uncertainty existing in some anatomical parts, it is difficult purely algorithm solve problem. Therefore, propose combine resulting...
1 Abstract Connectomics, the study of structure networks synaptically connected neurons, is one most important frontiers neuroscience. Great advances are being made on level macro- and meso-scale connectomics, that how which populations neurons wired together by tracing axons anatomically genetically defined throughout brain. Similarly, use electron-microscopy statistical connectome models has improved our understanding micro-connectomics, connectivity patterns between individual neurons. We...
Abstract The function of the neocortex is fundamentally determined by its repeating microcircuit motif, but also rich, interregional connectivity. We present a data-driven computational model anatomy non-barrel primary somatosensory cortex juvenile rat, integrating whole-brain scale data while providing cellular and subcellular specificity. consists 4.2 million morphologically detailed neurons, placed in digital brain atlas. They are connected 14.2 billion synapses, comprising local,...
Abstract Segmentation of brain tumor images is an important task in diagnosis and treatment planning for cancer patients. To achieve this goal with standard clinical acquisition protocols, conventionally, either classification algorithms are applied on multimodal MR or atlas‐based segmentation used a high‐resolution monomodal image. These two approaches have been commonly regarded separately. We propose to integrate all the available imaging information into one framework be able use gained...
Abstract The localization of clinically important points in brain images is crucial for many neurological studies. Conventional manual landmark annotation requires expertise and often time‐consuming. In this work, we propose an automatic approach interest point image using landmark‐annotated atlas (LAA). detection procedure formulated as a problem finding corresponding the atlas. LAA constructed from set with relevant landmarks annotated. It provides not only spatial information but also...