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Magnetoencephalography (MEG) and Electroencephalography (EEG) provide direct electrophysiological measures at an excellent temporal resolution, but the spatial resolution of source-reconstructed current activity is limited to several millimetres. Here we show, using simulations of MEG signals and Bayesian model comparison, that non-invasive myelin estimates from high-resolution quantitative magnetic resonance imaging (MRI) can enhance MEG/EEG source reconstruction. Our approach assumes that MEG/EEG signals primarily arise from the synchronised activity of pyramidal cells, and since most of the myelin in the cortical sheet originates from these cells, myelin density can predict the strength of cortical sources measured by MEG/EEG. Leveraging recent advances in quantitative MRI, we exploit this structure-function relationship and scale the leadfields of the forward model according to the local myelin density estimates from in vivo quantitative MRI to inform MEG/EEG source reconstruction. Using Bayesian model comparison and dipole localisation errors (DLEs), we demonstrate that adapting local forward fields to reflect increased local myelination at the site of a simulated source explains the simulated data better than models without such leadfield scaling. Our model comparison framework proves sensitive to myelin changes in simulations with exact coregistration and moderate-to-high sensor-level signal-to-noise ratios (≥10 dB) for the multiple sparse priors (MSP) and empirical Bayesian beamformer (EBB) approaches. Furthermore, we sought to infer the microstructure giving rise to specific functional activation patterns by comparing the myelin-informed model which was used to generate the activation with a set of test forward models incorporating different myelination patterns. We found that the direction of myelin changes, however not their magnitude, can be inferred by Bayesian model comparison. Finally, we apply myelin-informed forward models to MEG data from a visuo-motor experiment. We demonstrate improved source reconstruction accuracy using myelin estimates from a quantitative longitudinal relaxation (R1) map and discuss the limitations of our approach.
Highlights
We use quantitative MRI to implement myelin-informed forward models for M/EEG
Local myelin density was modelled by adapting the local leadfields
Myelin-informed forward models can improve source reconstruction accuracy
We can infer the directionality of myelination patterns, but not their strength
We apply our approach to MEG data from a visuo-motor experiment
Snapshots of acetyl-CoA synthesis, the final step of CO₂ fixation in the Wood-Ljungdahl pathway
(2024)
In the ancient microbial Wood-Ljungdahl pathway, CO2 is fixed in a multi-step process with acetyl-CoA synthesis at the bifunctional carbon monoxide dehydrogenase/acetyl-CoA synthase complex (CODH/ACS). Here, we present catalytic snapshots of the CODH/ACS from the gas-converting acetogen Clostridium autoethanogenum, characterizing the molecular choreography of the overall reaction including electron transfer to the CODH for CO2 reduction, methyl transfer from the corrinoid iron-sulfur protein (CoFeSP) partner to the ACS active site and acetyl-CoA production. Unlike CODH, the multidomain ACS undergoes large conformational changes to form an internal connection to the CODH active site, accommodate the CoFeSP for methyl transfer and protect the reaction intermediates. Altogether, the structures allow us to draw a detailed reaction mechanism of this enzyme crucial for CO2 fixation in anaerobic organisms.
The intensity and the features of sensory stimuli are encoded in the activity of neurons in the cortex. In the visual and piriform cortices, the stimulus intensity rescales the activity of the population without changing its selectivity for the stimulus features. The cortical representation of the stimulus is therefore intensity invariant. This emergence of network-invariant representations appears robust to local changes in synaptic strength induced by synaptic plasticity, even though (i) synaptic plasticity can potentiate or depress connections between neurons in a feature-dependent manner, and (ii) in networks with balanced excitation and inhibition, synaptic plasticity determines the nonlinear network behavior. In this study we investigate the consistency of invariant representations with a variety of synaptic states in balanced networks. By using mean-field models and spiking network simulations, we show how the synaptic state controls the emergence of intensity-invariant or intensity-dependent selectivity. In particular, we demonstrate that an effective power-law synaptic transformation at the population level is necessary for invariance. In a range of firing rates, purely depressing short-term synapses fulfills this condition, and in this case, the network is contrast-invariant. Instead, facilitating short-term plasticity generally narrows the network selectivity. We found that facilitating and depressing short-term plasticity can be combined to approximate a power-law that leads to contrast invariance. These results explain how the physiology of individual synapses is linked to the emergence of invariant representations of sensory stimuli at the network level.
Owing to their morphological complexity and dense network connections, neurons modify their proteomes locally, using mRNAs and ribosomes present in the neuropil (tissue enriched for dendrites and axons). Although ribosome biogenesis largely takes place in the nucleus and perinuclear region, neuronal ribosomal protein (RP) mRNAs have been frequently detected remotely, in dendrites and axons. Here, using imaging and ribosome profiling, we directly detected the RP mRNAs and their translation in the neuropil. Combining brief metabolic labeling with mass spectrometry, we found that a group of RPs rapidly associated with translating ribosomes in the cytoplasm and that this incorporation was independent of canonical ribosome biogenesis. Moreover, the incorporation probability of some RPs was regulated by location (neurites vs. cell bodies) and changes in the cellular environment (following oxidative stress). Our results suggest new mechanisms for the local activation, repair and/or specialization of the translational machinery within neuronal processes, potentially allowing neuronal synapses a rapid means to regulate local protein synthesis.
Owing to their morphological complexity and dense network connections, neurons modify their proteomes locally, using mRNAs and ribosomes present in the neuropil (tissue enriched for dendrites and axons). Although ribosome biogenesis largely takes place in the nucleus and perinuclear region, neuronal ribosomal protein (RP) mRNAs have been frequently detected remotely, in dendrites and axons. Here, using imaging and ribosome profiling, we directly detected the RP mRNAs and their translation in the neuropil. Combining brief metabolic labeling with mass spectrometry, we found that a group of RPs quickly associated with translating ribosomes in the cytoplasm and that this incorporation is independent of canonical ribosome biogenesis. Moreover, the incorporation probability of some RPs was regulated by location (neurites vs. cell bodies) and changes in the cellular environment (in response to oxidative stress). Our results suggest new mechanisms for the local activation, repair and/or specialization of the translational machinery within neuronal processes, potentially allowing remote neuronal synapses a rapid solution to the relatively slow and energy-demanding requirement of nuclear ribosome biogenesis.
Protein turnover, the net result of protein synthesis and degradation, enables cells to remodel their proteomes in response to internal and external cues. Previously, we analyzed protein turnover rates in cultured brain cells under basal neuronal activity and found that protein turnover is influenced by subcellular localization, protein function, complex association, cell type of origin, and by the cellular environment (Dörrbaum et al., 2018). Here, we advanced our experimental approach to quantify changes in protein synthesis and degradation, as well as the resulting changes in protein turnover or abundance in rat primary hippocampal cultures during homeostatic scaling. Our data demonstrate that a large fraction of the neuronal proteome shows changes in protein synthesis and/or degradation during homeostatic up- and down-scaling. More than half of the quantified synaptic proteins were regulated, including pre- as well as postsynaptic proteins with diverse molecular functions.
EphrinB2 and GRIP1 stabilize mushroom spines during denervation-induced homeostatic plasticity
(2021)
Highlights
• Denervation induces mushroom spine loss and AMPAR redistribution to the surface
• GRIP1 and ephrinB2 mediate homeostatic mechanisms after lesion
• Stimulation with the ephrinB2 receptor EphB4 promotes a surface shift of AMPARs
• AMPARs surface shift restores impaired spine recovery after lesion in GRIP1 mutants
Summary
Despite decades of work, much remains elusive about molecular events at the interplay between physiological and structural changes underlying neuronal plasticity. Here, we combined repetitive live imaging and expansion microscopy in organotypic brain slice cultures to quantitatively characterize the dynamic changes of the intracellular versus surface pools of GluA2-containing α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) across the different dendritic spine types and the shaft during hippocampal homeostatic plasticity. Mechanistically, we identify ephrinB2 and glutamate receptor interacting protein (GRIP) 1 as mediating AMPAR relocation to the mushroom spine surface following lesion-induced denervation. Moreover, stimulation with the ephrinB2 specific receptor EphB4 not only prevents the lesion-induced disappearance of mushroom spines but is also sufficient to shift AMPARs to the surface and rescue spine recovery in a GRIP1 dominant-negative background. Thus, our results unravel a crucial role for ephrinB2 during homeostatic plasticity and identify a potential pharmacological target to improve dendritic spine plasticity upon injury.
An important question concerning inter-areal communication in the cortex is whether these interactions are synergistic, i.e. brain signals can either share common information (redundancy) or they can encode complementary information that is only available when both signals are considered together (synergy). Here, we dissociated cortical interactions sharing common information from those encoding complementary information during prediction error processing. To this end, we computed co-information, an information-theoretical measure that distinguishes redundant from synergistic information among brain signals. We analyzed auditory and frontal electrocorticography (ECoG) signals in five common awake marmosets performing two distinct auditory oddball tasks and investigated to what extent event-related potentials (ERP) and broadband (BB) dynamics encoded redundant and synergistic information during auditory prediction error processing. In both tasks, we observed multiple patterns of synergy across the entire cortical hierarchy with distinct dynamics. The information conveyed by ERPs and BB signals was highly synergistic even at lower stages of the hierarchy in the auditory cortex, as well as between auditory and frontal regions. Using a brain-constrained neural network, we simulated the spatio-temporal patterns of synergy and redundancy observed in the experimental results and further demonstrated that the emergence of synergy between auditory and frontal regions requires the presence of strong, long-distance, feedback and feedforward connections. These results indicate that the distributed representations of prediction error signals across the cortical hierarchy can be highly synergistic.
Parallel multisite recordings in the visual cortex of trained monkeys revealed that the responses of spatially distributed neurons to natural scenes are ordered in sequences. The rank order of these sequences is stimulus-specific and maintained even if the absolute timing of the responses is modified by manipulating stimulus parameters. The stimulus specificity of these sequences was highest when they were evoked by natural stimuli and deteriorated for stimulus versions in which certain statistical regularities were removed. This suggests that the response sequences result from a matching operation between sensory evidence and priors stored in the cortical network. Decoders trained on sequence order performed as well as decoders trained on rate vectors but the former could decode stimulus identity from considerably shorter response intervals than the latter. A simulated recurrent network reproduced similarly structured stimulus-specific response sequences, particularly once it was familiarized with the stimuli through non-supervised Hebbian learning. We propose that recurrent processing transforms signals from stationary visual scenes into sequential responses whose rank order is the result of a Bayesian matching operation. If this temporal code were used by the visual system it would allow for ultrafast processing of visual scenes.
Solving the problem of consciousness remains one of the biggest challenges in modern science. One key step towards understanding consciousness is to empirically narrow down neural processes associated with the subjective experience of a particular content. To unravel these neural correlates of consciousness (NCC) a common scientific strategy is to compare perceptual conditions in which consciousness of a particular content is present with those in which it is absent, and to determine differences in measures of brain activity (the so called "contrastive analysis"). However, this comparison appears not to reveal exclusively the NCC, as the NCC proper can be confounded with prerequisites for and consequences of conscious processing of the particular content. This implies that previous results cannot be unequivocally interpreted as reflecting the neural correlates of conscious experience. Here we review evidence supporting this conjecture and suggest experimental strategies to untangle the NCC from the prerequisites and consequences of conscious experience in order to further develop the otherwise valid and valuable contrastive methodology.