Efficacy of adrenal venous sampling is increased by point of care cortisol analysis

Primary aldosteronism (PA) is a common cause of secondary hypertension and is caused by unilateral or bilateral adrenal disease. Treatment options depend on whether the disease is lateralized or not, which is preferably evaluated with selective adrenal venous sampling (AVS). This procedure is technically challenging, and obtaining representative samples from the adrenal veins can prove difficult. Unsuccessful AVS procedures often require reexamination. Analysis of cortisol during the procedure may enhance the success rate. We invited 21 consecutive patients to participate in a study with intra-procedural point of care cortisol analysis. When this assay showed nonrepresentative sampling, new samples were drawn after redirection of the catheter. The study patients were compared using the 21 previous procedures. The intra-procedural cortisol assay increased the success rate from 10/21 patients in the historical cohort to 17/21 patients in the study group. In four of the 17 successful procedures, repeated samples needed to be drawn. Successful sampling at first attempt improved from the first seven to the last seven study patients. Point of care cortisol analysis during AVS improves success rate and reduces the need for reexaminations, in accordance with previous studies. Successful AVS is crucial when deciding which patients with PA will benefit from surgical treatment.publishedVersio

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Biofysisk modellering av EEG-signaler fra nerveceller i hjernen

In order to understand the link between brain signal recordings, such as electrocorticography (ECoG) and electroencephalography (EEG), and the undellying neural activity, neuroinformatics tools play an important role. A great example of such a tool is the open-source Python package, LFPy, which can be used for numerical calculations of extracellular potentials, based on a well-established compartment-based forward-modeling scheme. In this project, detailed biophysical modeling was used to gain a better understanding of contributions from single neurons to measurable extracellular potentials. In particular, we addressed the following questions: How do single neurons contribute to ECoG and EEG signals? And can these signals be modeled with the current dipole approximation? Python tools for calculating neural axial currents and the current dipole moment of a neuron were developed, and further built on to calculate extracellular potentials from the current dipole approximation. These methods, in addition to the above-mentioned compartment-based forward model, were used for studying extracellular potentials from single-cell simulations. The two different models give similar results for computations of extracellular recordings from virtual electrodes placed several millimeters away from the neuron source. Thus, the dipole approximation cannot be used for predicting single-cell contributions to ECoG signals, since ECoG recordings are measured only some hundred micrometers away from the neuron. For modeling of single-cell EEG contributions, on the other hand, the current dipole approxima- tion appears to be applicable. SAMMENDRAG: For å forstå sammenhengen mellom elektrisk aktivitet i nervecellene i hjernen og målinger som elektrokortikografi (ECoG) og elektroencefalogram (EEG), er informatikkverktøy en viktig bidragsyter. LFPy er et godt eksempel på et slikt verktøy, utviklet som en tilleggspakke i Python med åpen kildekode. Blant annet kan LFPy brukes til å regne ut elektriske potensialer med utgangspunkt i en veletablert metode for compartment-basert direkte modellering. I denne oppgaven blir detaljert biofysisk modellering av enkeltnerveceller brukt til å undersøke hvordan disse bidrar til målbare ekstracellulære potensialer. Særlig belyses følgende spørsmål: På hvilken måte bidrar enkeltnerveceller til ECoG- og EEG-signaler? Og kan disse signalene modelleres ved hjelp av strømdipolmoment-tilnærmingen? Python-verktøy for utregning av nevrale aksial-strømmer og strømdipolmoment fra nerveceller ble utviklet og videre brukt til å implementere dipol-basert direkte modellering i Python. Dette kan, sammen med den ovennevnte compartment-baserte modelleringsmetoden, brukes til å undersøke bidraget til ECoG- og EEG-signaler fra enkelthjerneceller. Resultatene fra de to forskjellige modellene viste seg å være like når målepunktene ble plassert flere millimeter unna nervecellen. Følgelig kan ikke strømdipolmoment brukes til å modellere enkeltcellebidrag til ECoG-signaler, siden nervecellen her ligger kun noen hundre mikrometer unna målepunktet. Bidrag til EEG-signaler kan derimot i stor grad forutsies ved hjelp av strømdipol-tilnærmingen.M-M

Biofysisk modellering av EEG-signaler fra nerveceller i hjernen

In order to understand the link between brain signal recordings, such as electrocorticography (ECoG) and electroencephalography (EEG), and the undellying neural activity, neuroinformatics tools play an important role. A great example of such a tool is the open-source Python package, LFPy, which can be used for numerical calculations of extracellular potentials, based on a well-established compartment-based forward-modeling scheme. In this project, detailed biophysical modeling was used to gain a better understanding of contributions from single neurons to measurable extracellular potentials. In particular, we addressed the following questions: How do single neurons contribute to ECoG and EEG signals? And can these signals be modeled with the current dipole approximation? Python tools for calculating neural axial currents and the current dipole moment of a neuron were developed, and further built on to calculate extracellular potentials from the current dipole approximation. These methods, in addition to the above-mentioned compartment-based forward model, were used for studying extracellular potentials from single-cell simulations. The two different models give similar results for computations of extracellular recordings from virtual electrodes placed several millimeters away from the neuron source. Thus, the dipole approximation cannot be used for predicting single-cell contributions to ECoG signals, since ECoG recordings are measured only some hundred micrometers away from the neuron. For modeling of single-cell EEG contributions, on the other hand, the current dipole approxima- tion appears to be applicable. SAMMENDRAG: For å forstå sammenhengen mellom elektrisk aktivitet i nervecellene i hjernen og målinger som elektrokortikografi (ECoG) og elektroencefalogram (EEG), er informatikkverktøy en viktig bidragsyter. LFPy er et godt eksempel på et slikt verktøy, utviklet som en tilleggspakke i Python med åpen kildekode. Blant annet kan LFPy brukes til å regne ut elektriske potensialer med utgangspunkt i en veletablert metode for compartment-basert direkte modellering. I denne oppgaven blir detaljert biofysisk modellering av enkeltnerveceller brukt til å undersøke hvordan disse bidrar til målbare ekstracellulære potensialer. Særlig belyses følgende spørsmål: På hvilken måte bidrar enkeltnerveceller til ECoG- og EEG-signaler? Og kan disse signalene modelleres ved hjelp av strømdipolmoment-tilnærmingen? Python-verktøy for utregning av nevrale aksial-strømmer og strømdipolmoment fra nerveceller ble utviklet og videre brukt til å implementere dipol-basert direkte modellering i Python. Dette kan, sammen med den ovennevnte compartment-baserte modelleringsmetoden, brukes til å undersøke bidraget til ECoG- og EEG-signaler fra enkelthjerneceller. Resultatene fra de to forskjellige modellene viste seg å være like når målepunktene ble plassert flere millimeter unna nervecellen. Følgelig kan ikke strømdipolmoment brukes til å modellere enkeltcellebidrag til ECoG-signaler, siden nervecellen her ligger kun noen hundre mikrometer unna målepunktet. Bidrag til EEG-signaler kan derimot i stor grad forutsies ved hjelp av strømdipol-tilnærmingen.M-M

Biophysical Modeling of EEG Signals from Neurons in the Brain

In order to understand the link between brain signal recordings, such as electrocorticography (ECoG) and electroencephalography (EEG), and the undellying neural activity, neuroinformatics tools play an important role. A great example of such a tool is the open-source Python package, LFPy, which can be used for numerical calculations of extracellular potentials, based on a well-established compartment-based forward-modeling scheme. In this project, detailed biophysical modeling was used to gain a better understanding of contributions from single neurons to measurable extracellular potentials. In particular, we addressed the following questions: How do single neurons contribute to ECoG and EEG signals? And can these signals be modeled with the current dipole approximation? Python tools for calculating neural axial currents and the current dipole moment of a neuron were developed, and further built on to calculate extracellular potentials from the current dipole approximation. These methods, in addition to the above-mentioned compartment-based forward model, were used for studying extracellular potentials from single-cell simulations. The two different models give similar results for computations of extracellular recordings from virtual electrodes placed several millimeters away from the neuron source. Thus, the dipole approximation cannot be used for predicting single-cell contributions to ECoG signals, since ECoG recordings are measured only some hundred micrometers away from the neuron. For modeling of single-cell EEG contributions, on the other hand, the current dipole approxima- tion appears to be applicable. SAMMENDRAG: For å forstå sammenhengen mellom elektrisk aktivitet i nervecellene i hjernen og målinger som elektrokortikografi (ECoG) og elektroencefalogram (EEG), er informatikkverktøy en viktig bidragsyter. LFPy er et godt eksempel på et slikt verktøy, utviklet som en tilleggspakke i Python med åpen kildekode. Blant annet kan LFPy brukes til å regne ut elektriske potensialer med utgangspunkt i en veletablert metode for compartment-basert direkte modellering. I denne oppgaven blir detaljert biofysisk modellering av enkeltnerveceller brukt til å undersøke hvordan disse bidrar til målbare ekstracellulære potensialer. Særlig belyses følgende spørsmål: På hvilken måte bidrar enkeltnerveceller til ECoG- og EEG-signaler? Og kan disse signalene modelleres ved hjelp av strømdipolmoment-tilnærmingen? Python-verktøy for utregning av nevrale aksial-strømmer og strømdipolmoment fra nerveceller ble utviklet og videre brukt til å implementere dipol-basert direkte modellering i Python. Dette kan, sammen med den ovennevnte compartment-baserte modelleringsmetoden, brukes til å undersøke bidraget til ECoG- og EEG-signaler fra enkelthjerneceller. Resultatene fra de to forskjellige modellene viste seg å være like når målepunktene ble plassert flere millimeter unna nervecellen. Følgelig kan ikke strømdipolmoment brukes til å modellere enkeltcellebidrag til ECoG-signaler, siden nervecellen her ligger kun noen hundre mikrometer unna målepunktet. Bidrag til EEG-signaler kan derimot i stor grad forutsies ved hjelp av strømdipol-tilnærmingen

Biophysical modeling of electric and magnetic brain signals

When we think and feel, the nerve cells (neurons) in the brain communicate by means of electric messages. We can listen to these neural conversations by recording resulting electric and magnetic signals on the outside of the head. The electric signals can be measured with small electrodes placed on the scalp, a method known as electroencephalography (EEG), while magnetic fields can be recorded with magnetoencephalography (MEG). Even though EEG and MEG are widely used techniques for studying cognition and disease in the human brain, we know surprisingly little about the neural origin of these signals. We can get an overview of the electrical activity in the neural symphony by studying the current dipole moment capturing the melody of the network. To illustrate: if you know which tune is played on stage, you will have a good idea of what one can hear from the outside of the concert hall. Correspondingly, the current dipole moment can be applied for modeling EEG and MEG signals measured outside of the head. Furthermore, it is possible to simulate neural activity with detailed neuron models reconstructed from experimental data. However, the possibility to predict non-invasive brain recordings by calculating the current dipole moment from detailed neural activity has not yet been taken full advantage of. This thesis presents a forward modeling framework for computing EEG and MEG signals, with methods firmly grounded in the underlying biophysics. Specifically, In Paper I, we present analytical formulas and available python code for computing electric brain signals from a current dipole moment in a simplified head consisting of four concentric spheres. In Paper II, we expand the open-source python-package LFPy, allowing for current dipole calculations from morphologically reconstructed neurons and neural populations. LFPy 2.0 includes methods for computing electric potentials on top of the brain (electrocorticography), as well as EEG and MEG signals. In Paper III, we apply methods from Paper I and II to compute the current dipole moment and the resulting electric brain signals from biophysically detailed single cells and existing neural simulations. We demonstrate how the presented modeling framework opens the door for exploring the neural origin of electric and magnetic brain signals

Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0

Recordings of extracellular electrical, and later also magnetic, brain signals have been the dominant technique for measuring brain activity for decades. The interpretation of such signals is however nontrivial, as the measured signals result from both local and distant neuronal activity. In volume-conductor theory the extracellular potentials can be calculated from a distance-weighted sum of contributions from transmembrane currents of neurons. Given the same transmembrane currents, the contributions to the magnetic field recorded both inside and outside the brain can also be computed. This allows for the development of computational tools implementing forward models grounded in the biophysics underlying electrical and magnetic measurement modalities. LFPy (LFPy.readthedocs.io) incorporated a well-established scheme for predicting extracellular potentials of individual neurons with arbitrary levels of biological detail. It relies on NEURON (neuron.yale.edu) to compute transmembrane currents of multicompartment neurons which is then used in combination with an electrostatic forward model. Its functionality is now extended to allow for modeling of networks of multicompartment neurons with concurrent calculations of extracellular potentials and current dipole moments. The current dipole moments are then, in combination with suitable volume-conductor head models, used to compute non-invasive measures of neuronal activity, like scalp potentials (electroencephalographic recordings; EEG) and magnetic fields outside the head (magnetoencephalographic recordings; MEG). One such built-in head model is the four-sphere head model incorporating the different electric conductivities of brain, cerebrospinal fluid, skull and scalp. We demonstrate the new functionality of the software by constructing a network of biophysically detailed multicompartment neuron models from the Neocortical Microcircuit Collaboration (NMC) Portal (bbp.epfl.ch/nmc-portal) with corresponding statistics of connections and synapses, and compute in vivo-like extracellular potentials (local field potentials, LFP; electrocorticographical signals, ECoG) and corresponding current dipole moments. From the current dipole moments we estimate corresponding EEG and MEG signals using the four-sphere head model. We also show strong scaling performance of LFPy with different numbers of message-passing interface (MPI) processes, and for different network sizes with different density of connections. The open-source software LFPy is equally suitable for execution on laptops and in parallel on high-performance computing (HPC) facilities and is publicly available on GitHub.com.Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0publishedVersio