Tobias Rose

Tobias Rose
University of Bonn | Uni Bonn · Institute of Experimental Epileptology and Cognition Research

Professor

About

30
Publications
23,368
Reads
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1,320
Citations
Additional affiliations
October 2010 - present
Max Planck Institute of Neurobiology
Position
  • Group Leader
March 2006 - October 2010
Friedrich Miescher Institute for Biomedical Research
Position
  • PostDoc Position
November 2002 - March 2006
European Neuroscience Institute Göttingen
Position
  • PhD Student

Publications

Publications (30)
Article
At synapses formed between dissociated neurons, about half of all synaptic vesicles are refractory to evoked release, forming the so-called "resting pool." Here, we use optical measurements of vesicular pH to study developmental changes in pool partitioning and vesicle cycling in cultured hippocampal slices. Two-photon imaging of a genetically enco...
Article
Ocular dominance plasticity reconsidered How neuronal circuits are established and reformed during development and learning is unclear. One idea is that cortical circuits have virtually unlimited plasticity and are rebuilt routinely from random components. An alternative view is that some of these connections are more or less preformed and rigid. W...
Article
Full-text available
The brain extracts behaviourally relevant sensory input to produce appropriate motor output. On the one hand, our constantly changing environment requires this transformation to be plastic. On the other hand, plasticity is thought to be balanced by mechanisms ensuring constancy of neuronal representations in order to achieve stable behavioural perf...
Article
Full-text available
Experience-dependent plasticity in the mature visual system is widely considered to be cortical. Using chronic two-photon Ca2+ imaging of thalamic afferents in layer 1 of binocular visual cortex, we provide evidence against this tenet: the respective dorsal lateral geniculate nucleus (dLGN) cells showed pronounced ocular dominance (OD) shifts after...
Article
Segregation of retinal ganglion cell (RGC) axons by type and eye of origin is considered a hallmark of dorsal lateral geniculate nucleus (dLGN) structure. However, recent anatomical studies have shown that neurons in mouse dLGN receive input from multiple RGC types of both retinae. Whether convergent input leads to relevant functional interactions...
Preprint
Full-text available
Representational drift - the gradual continuous change of neuronal representations - has been observed across many brain areas. It is unclear whether this drift is caused by synaptic plasticity elicited by sensory experience, or by the intrinsic volatility of synapses. Here, using chronic two-photon calcium imaging in mouse primary visual cortex, w...
Article
Full-text available
Pyramidal cells of neocortical layer 2/3 (L2/3 PyrCs) integrate signals from numerous brain areas and project throughout the neocortex. These PyrCs show pial depth-dependent functional and structural specializations, indicating participation in different functional microcircuits. However, whether these depth-dependent differences result from separa...
Article
Full-text available
The functional properties of neocortical pyramidal cells (PCs), such as direction and orientation selectivity in visual cortex, predominantly derive from their excitatory and inhibitory inputs. For layer 2/3 (L2/3) PCs, the detailed relationship between their functional properties and how they sample and integrate information across cortical space...
Article
Full-text available
Significance The investigation of the topographic organization of spatially coding cell types in the medial entorhinal cortex (MEC) has so far been held back by the lack of appropriate tools that enable the precise recording of both the anatomical location and activity of large populations of cells while animals forage in open environments. In this...
Preprint
Full-text available
Pyramidal cells of neocortical layer 2/3 (L2/3 PyrCs) integrate signals from numerous brain areas and project throughout the neocortex. Within L2/3, PyrCs show functional and structural specializations depending on their pial depth, indicating participation in different functional microcircuits. However, it is unknown whether these depth-dependent...
Article
The response of individual neurons to stable sensory input or behavioral output can change over time. A new study provides evidence from the mouse visual system that such drift does not follow the hierarchy of information flow across the brain.
Preprint
Full-text available
The medial entorhinal cortex (MEC) creates a map of local space, based on the firing patterns of grid, head direction (HD), border, and object-vector (OV) cells. How these cell types are organized anatomically is debated. In-depth analysis of this question requires collection of precise anatomical and activity data across large populations of neuro...
Preprint
Full-text available
Eye-specific segregation of retinal ganglion cell (RGC) axons in the dorsal lateral geniculate nucleus (dLGN) is considered a hallmark of visual system development. However, a recent anatomical study showed that nearly half of the neurons in dLGN of adult mice still receive input from both retinae, but functional data about binocularity in mature d...
Preprint
Full-text available
Neocortical pyramidal cells (PCs) display functional specializations defined by their excitatory and inhibitory circuit connectivity. For layer 2/3 (L2/3) PCs, little is known about the detailed relationship between their neuronal response properties, dendritic structure and their underlying circuit connectivity at the level of single cells. Here,...
Article
In vivo two-photon calcium imaging provides detailed information about the activity and response properties of individual neurons. However, in vitro methods are often required to study the underlying neuronal connectivity and physiology at the cellular and synaptic levels at high resolution. This protocol provides a fast and reliable workflow for c...
Article
Experience-dependent plasticity in the visual system is traditionally thought to be exclusively cortical whereas the dorsal lateral geniculate nucleus (dLGN) is classically considered to just be a 'relay' of visual information between the retina and the cortex. However, a number of recent experiments call into question the simplistic view of visual...
Article
Full-text available
A sophisticated analysis in mice of how inputs to neurons from other neurons are distributed across individual cells of the brain’s visual cortex provides information about how mammalian vision is processed.
Article
Full-text available
We summarize here the results presented and subsequent discussion from the meeting on Integrating Hebbian and Homeostatic Plasticity at the Royal Society in April 2016. We first outline the major themes and results presented at the meeting. We next provide a synopsis of the outstanding questions that emerged from the discussion at the end of the me...
Article
Full-text available
Experience-dependent plasticity in the mature visual system is considered exclusively cortical. Using chronic two-photon Ca2+ imaging, we found evidence against this tenet: dLGN cells showed robust ocular dominance shifts after monocular deprivation. Most, but not all responses of dLGN cell boutons in binocular visual cortex were monocular during b...
Article
Full-text available
Studies on the cellular function of the pancreas are typically performed in vitro on its isolated functional units, the endocrine islets of Langerhans and the exocrine acini. However, these approaches are hampered by preparation-induced changes of cell physiology and the lack of an intact surrounding. We present here a detailed protocol for the pre...
Article
Full-text available
More than a decade ago genetically encoded calcium indicators (GECIs) entered the stage as new promising tools to image calcium dynamics and neuronal activity in living tissues and designated cell types in vivo. From a variety of initial designs two have emerged as promising prototypes for further optimization: FRET (Förster Resonance Energy Transf...
Article
Full-text available
GABAB receptors are the G-protein-coupled receptors for GABA, the main inhibitory neurotransmitter in the brain. GABAB receptors are abundant on dendritic spines, where they dampen postsynaptic excitability and inhibit Ca2+ influx through NMDA receptors when activated by spillover of GABA from neighboring GABAergic terminals. Here, we show that an...
Data
MOESM1 [Supplementary Fig. 1. Spatial selectivity of spike initiation breaks down at laser powers > 300 µW. A ChR2-transfected CA1 pyramidal cell was stimulated in a line pattern perpendicular to the orientation of the apical dendrite with 10 ms laser pulses at 3 different laser intensities. The soma was positioned in the middle]
Article
Full-text available
Over the past few years, the light-gated cation channel Channelrhodopsin-2 (ChR2) has seen a remarkable diversity of applications in neuroscience. However, commonly used wide-field illumination provides poor spatial selectivity for cell stimulation. We explored the potential of focal laser illumination to map photocurrents of individual neurons in...
Article
Full-text available
The Goto Kakizaki (GK) rat is a widely used animal model to study defective glucose-stimulated insulin release in type-2 diabetes (T2D). As in T2D patients, the expression of several proteins involved in Ca(2+)-dependent exocytosis of insulin-containing large dense-core vesicles is dysregulated in this model. So far, a defect in late steps of insul...
Article
Full-text available
We report on factors affecting the spontaneous firing pattern of the identified serotonin-containing Retzius neurons of the medicinal leech. Increased firing activity induced by intracellular current injection is followed by a 'post-stimulus-depression' (PSD) without spiking for up to 23 s. PSD duration depends both on the duration and the amplitud...
Article
Cyclic AMP regulates Ca(2+)-dependent exocytosis through a classical protein kinase A (PKA)-dependent and an alternative cAMP-guanine nucleotide exchange factor (GEF)/Epac-dependent pathway in many secretory cells. Although increased cAMP is believed to double secretory output in isolated pituitary cells, the direct target(s) for cAMP action and a...
Article
In pancreatic beta-cells, inhibition of K(ATP)-channels plays a pivotal role in signal transduction of glucose-induced insulin release. However, the extreme sensitivity of K(ATP)-channels to its ligand ATP as found in inside-out patches is not directly compatible with modulation of these channels at physiological [ATP](i). We studied K(ATP)-channel...

Questions

Question (1)
Question
Dear all,
I have a (slightly long) statistics / data science question concerning the applicability, implementation and concise reporting of a log-linear model.
I am also open for alternative suggestions.
Data:
We recorded activity of neurons in different brain regions, different cell types within these regions and using different stimuli. For each of these factors we get different numbers of neurons which we classify based on responsiveness criteria to belong to three groups.
So we have a multi-level contingency table with:
R1-3 = Response variable (3 level) - frequency
Several multilevel conditions:
stimulus type (S1-3), cell type (C1-2), brain region (B1-2).
My questions:
1. is a log-linear model the analysis of choice here - or am I completely off?
2. is my model selection and comparison reasonable?
3. is focusing on interaction only and disregarding main effects (which are trivial here based on random sampling over conditions) sound?
4. How should I report on model selection and interaction relevance in the most concise way possible (severe space limitations in publication)?
- - - - -
I chose to analyze this with a multifactorial log linear model and test for interactions (which may be incorrect - pls advice).
The independence model would be:
log(freq) = log(R) + log(S) + log(C) + log(B)
R:
mod0 <- glm(Freq ~ R + S + C + B,
data = data.df, family = poisson)
The conditions themselves should be independent (or of no interest for us) but there may be interactions with the Response partitioning which we would like to test for. So I make single pairwise interaction models:
1. log(freq) = log(R) + (log(R) * log(S)) + log(C) + log(B)
2. log(freq) = log(R) + log(S) + (log(R) * log(C)) + log(B)
3. log(freq) = log(R) + log(S) + log(C) + (log(R) * log(B))
R:
mod1 <- glm(Freq ~ R + (R * S) + C + B,
data = data.df, family = poisson)
...
Alternatively I make a multiple pairwise interaction model with all of these interactions:
4. log(freq) = log(R) + (log(R) * log(S)) + (log(R) * log(C)) + (log(R) * log(B))
R:
mod4 <- glm(Freq ~ R + (R * S) + (R * C) + (R * B),
data = data.df, family = poisson)
I am not interested in the model fits themselves but in the interactions of the conditions with the relative partitioning into the three response groups. The cell numbers recorded under the different conditions vary based on different sampling.
My rationale is to compare the goodness of fit statistics of the single or multiple interaction models to the independence model, disregarding the actual raw fit results given the essentially arbitrary sampling across conditions.
The most concise (and probably far to simplistic) way of reporting the test results could either be
a) raw Chi2 p-value of the model comparison statistics using ANOVA
e.g:
R
anova(mod1, mod0, test="Chi"))
or
b) reporting of the different p-values of the specific interaction coefficients of the models
e.g.:
R
summary(mod4)
R1:S2 - Pr(>|z|) = 0.0001 ***
R1:S3 - Pr(>|z|) = 0.1
R1:C1 - Pr(>|z|) = 0.01 **
R1:B1 - Pr(>|z|) = 0.1
Or should I report full statistics on individual model GOF (G2, df, coefficient estimates, individual p-values etc.)?
My questions (again)
1. is a log-linear model the analysis of choice here - or am I completely off?
2. is my model selection and comparison reasonable?
3. is focusing on interaction only and disregarding main effects (which are trivial here based on random sampling over conditions) sound?
4. How should I report on model selection and interaction relevance in the most concise way possible (severe space limitations in publication)?
Thanks!
T

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